ACL 2024 Papers

The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools. However, existing benchmarks typically focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization. To address this issue, we present UltraTool, a novel benchmark designed to improve and evaluate LLMs’ ability in tool utilization within real-world scenarios. UltraTool focuses on the entire process of using tools - from planning and creating to applying them in complex tasks. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage and simplifies the task solving by mapping out the intermediate steps. Thus, unlike previous work, it eliminates the restriction of pre-defined toolset. Through extensive experiments on various LLMs, we offer novel insights into the evaluation of capabilities of LLMs in tool utilization, thereby contributing a fresh perspective to this rapidly evolving field. The benchmark is publicly available at https://github.com/JoeYing1019/UltraTool.

In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engineering to the integration of policy networks, either face efficiency issues or deliver suboptimal performance. Inspired by the dual-process theory in psychology, which identifies two distinct modes of thinking—intuitive (fast) and analytical (slow), we propose the Dual-Process Dialogue Planning (DPDP) framework. DPDP embodies this theory through two complementary planning systems: an instinctive policy model for familiar contexts and a deliberative Monte Carlo Tree Search (MCTS) mechanism for complex, novel scenarios. This dual strategy is further coupled with a novel two-stage training regimen: offline Reinforcement Learning for robust initial policy model formation followed by MCTS-enhanced on-the-fly learning, which ensures a dynamic balance between efficiency and strategic depth. Our empirical evaluations across diverse dialogue tasks affirm DPDP’s superiority in achieving both high-quality dialogues and operational efficiency, outpacing existing methods.

In the field of education, for better assessment of students’ abilities, generated questions often need to meet experts’ requirements, indicating the need for controllable question generation (CQG). However, current CQG methods mainly focus on difficulty control, neglecting the control of question content and assessed abilities, which are also crucial in educational QG. In this paper, we propose an LLM-guided method PFQS (for Planning First, Question Second), which utilizes Llama 2 to generate an answer plan and then generates questions based on it. The plan not only includes candidate answers but also integrates LLM’s understanding and multiple requirements, which make question generation simple and controllable. We evaluate our approach on the FairytaleQA dataset, a well-structured QA dataset derived from child-friendly storybooks. In the dataset, the attribute label represents content control, while the local_or_sum and ex_or_im labels denote difficulty control. Experimental results demonstrate that our approach outperforms previous state-of-the-art results and achieves better consistency with requirements compared to prompt-based method. Further application of our method to Llama 2 and Mistral also leads to improved requirement consistency in a zero-shot setting.

Data augmentation methods have been a promising direction to improve the performance of small models for low-resource dialogue state tracking. However, traditional methods rely on pre-defined user goals and neglect the importance of data complexity in this task. In this paper, we propose EDZ-DA, an Easy-to-Difficult Zero-shot Data Augmentation framework for low-resource dialogue state tracking that utilizes large language models to automatically catch the relationships of different domains and then generate the dialogue data. We also complicate the dialogues based on the domain relation to enhance the model’s capability for co-reference slot tracking. Furthermore, we permute slot values to mitigate the influence of output orders and the problem of incomplete value generation. Experimental results illustrate the superiority of our proposed method compared to previous strong data augmentation baselines on MultiWOZ.

Table-to-text generation involves generating appropriate textual descriptions given structured tabular data. It has attracted increasing attention in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. A common feature across existing methods is their treatment of the input as a string, i.e., by employing linearization techniques that do not always preserve information in the table, are verbose, and lack space efficiency. We propose to rethink data-to-text generation as a visual recognition task, removing the need for rendering the input in a string format. We present PixT3, a multimodal table-to-text model that overcomes the challenges of linearization and input size limitations encountered by existing models. PixT3 is trained with a new self-supervised learning objective to reinforce table structure awareness and is applicable to open-ended and controlled generation settings. Experiments on the ToTTo and Logic2Text benchmarks show that PixT3 is competitive and, in some settings, superior to generators that operate solely on text.

Aspect sentiment quad prediction (ASQP) has garnered significant attention in aspect-based sentiment analysis (ABSA). Current ASQP research primarily relies on pre-trained generative language models to produce templated sequences, often complemented by grid-based auxiliary methods. Despite these efforts, the persistent challenge of generation instability remains unresolved and the effectiveness of grid methods remains underexplored in current studies. To this end, we introduce Grid Noise Diffusion Pinpoint Network (GDP), a T5-based generative model aiming to tackle the issue of generation instability. The model consists of three novel modules, including Diffusion Vague Learning (DVL) to facilitate effective model learning and enhance overall robustness; Consistency Likelihood Learning (CLL) to discern the characteristics and commonalities of sentiment elements and thus reduce the impact of distributed noise; and GDP-FOR, a novel generation template, to enable models to generate outputs in a more natural way. Extensive experiments on four datasets demonstrate the remarkable effectiveness of our approach in addressing ASQP tasks.

Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To address this, we introduce WinoVis, a novel dataset specifically designed to probe text-to-image models on pronoun disambiguation within multimodal contexts. Utilizing GPT-4 for prompt generation and Diffusion Attentive Attribution Maps (DAAM) for heatmap analysis, we propose a novel evaluation framework that isolates the models’ ability in pronoun disambiguation from other visual processing challenges. Evaluation of successive model versions reveals that, despite incremental advancements, Stable Diffusion 2.0 achieves a precision of 56.7% on WinoVis, only marginally surpassing random guessing. Further error analysis identifies important areas for future research aimed at advancing text-to-image models in their ability to interpret and interact with the complex visual world.

Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training data, LLMs are not very well equipped for tasks involving structured data generation. We propose a framework, Prompting with Iterative Verification (PiVe), to improve graph-based generative capability of LLMs. We show how a small language model could be trained to act as a verifier module for the output of an LLM(i.e., ChatGPT, GPT-4), and to iteratively improve its performance via fine-grained corrective instructions. We also show how the verifier module could apply iterative corrections offline for a more cost-effective solution to the text-to-graph generation task. Experiments on three graph-based datasets show consistent improvement gained via PiVe. Additionally, we create GenWiki-HIQ and highlight that the verifier module can be used as a data augmentation tool to help improve the quality of automatically generated parallel text-graph datasets.

Instruction Fine-Tuning, a method enhancing pre-trained language models’ capabilities from mere next-word prediction to complex instruction following, often employs a one-off training approach on diverse instruction dataset. However, this method may not effectively enhance models’ adherence to instructions due to the simultaneous handling of varying instruction complexities. To address this, we propose a novel phased instruction fine-tuning (Phased IFT) method, grounded in the hypothesis of progressive alignment, which posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process. Specifically, we obtain the score of difficulty for each instruction via GPT-4, stratify the instruction data into subsets of increasing difficulty, and sequentially uptrain on these subsets using the standard supervised loss. Through extensive experiments on the pre-trained models Llama-2 7B/13B, and Mistral-7B using the 52K Alpaca instruction data, we demonstrate that Phased IFT significantly surpasses traditional one-off instruction fine-tuning (One-off IFT) method in win rate, empirically validating the progressive alignment hypothesis. Our findings suggest that Phased IFT offers a simple yet effective pathway for elevating the instruction-following capabilities of pre-trained language models.

The rapid growth in natural language processing (NLP) research has led to numerous new models, outpacing our understanding of how they compare to established ones. One major reason for this difficulty is saturating benchmarks, which may not well reflect differences in model performance in the wild. In this work, we introduce a novel framework to compare two NLP models by revealing their shared invariance to interpretable input perturbations targeting a specific linguistic capability. Via experiments on models from the same and different architecture families, this framework offers insights about how changes in models (e.g., distillation, size increase) affect linguistic capabilities. Furthermore, our framework enables evaluation of invariances between commercial black-box models (e.g., InstructGPT family) and models that are better understood (e.g., GPT-2). Across experiments, we observe that large language models share many invariances encoded by models of various sizes, whereas the invariances by large models are only shared by other large models. Possessing a wide variety of invariances may be key to the recent successes of large language models, and our framework can shed light on the types of invariances retained or emerging in new models. We make the code publicly available.

Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs). Previous works focus on retrieval-based models or generative models in a specific domain due to a lack of data across multiple domains. In this paper, we leverage GPT-4 to create the first multi-domain persuasive dialogue dataset DailyPersuasion. Then we propose a general method named PersuGPT to learn a persuasion model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user’s utterance and reasons next strategy to respond. Moreover, we design a simulation-based preference optimization, which utilizes a learned user model and our model to simulate next turns and estimate their rewards more accurately. Experimental results on two datasets indicate that our proposed method outperforms all baselines in terms of automatic evaluation metric Win-Rate and human evaluation. The code and data are available at https://persugpt.github.io.

Although language model performance across diverse tasks continues to improve, these models still struggle to understand and explain the beliefs of other people. This skill requires perspective-taking, the process of conceptualizing the point of view of another person. Perspective taking becomes challenging when the text reflects more personal and potentially more controversial beliefs.We explore this task through natural language generation of responses to conflict situations. We evaluate novel modifications to recent architectures for conditioning generation on an individual’s comments and self-disclosure statements. Our work extends the Social-Chem-101 corpus, using 95k judgements written by 6k authors from English Reddit data, for each of whom we obtained 20-500 self-disclosure statements. Our evaluation methodology borrows ideas from both personalized generation and theory of mind literature. Our proposed perspective-taking models outperform recent work, especially the twin encoder model conditioned on self-disclosures with high similarity to the conflict situation.

Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations. Most existing works utilize side information (e.g. topics or personas) in isolation to enhance the topic selection ability. However, due to disregarding the noise within these auxiliary information sources and their mutual influence, current models tend to predict user-uninteresting and contextually irrelevant topics. To build user-engaging and coherent dialogue agent, we propose a personalized topic selection model for topic-grounded dialogue, named PETD, which takes account of the interaction of side information to selectively aggregate such information for more accurately predicting subsequent topics. Specifically, we evaluate the correlation between global topics and personas and selectively incorporate the global topics aligned with user personas. Furthermore, we propose a contrastive learning based persona selector to filter relevant personas under the constraint of lacking pertinent persona annotations. Throughout the selection and generation, diverse relevant side information is considered. Extensive experiments demonstrate that our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines across various evaluation metrics.

English NLP systems have empirically worse performance for dialects other than Standard American English (SAmE). However, how these discrepancies impact use of language technology by speakers of non-SAmE global Englishes is not well understood. We focus on reducing this gap for South Asian Englishes (SAsE), a macro-group of regional varieties with cumulatively more speakers than SAmE, by surveying SAsE speakers about their interactions with language technology and compare their responses to a control survey of SAmE speakers. SAsE speakers are more likely to recall failures with language technology and more likely to reference specific issues with written language technology than their SAmE counterparts. Furthermore, SAsE speakers indicate that they modify both their lexicon and syntax to make technology work better, but that lexical issues are perceived as the most salient challenge. We then assess whether these issues are pervasive in more recently developed Large Language Models (LLMs), introducing two benchmarks for broader SAsE Lexical and Indian English Syntactic understanding and evaluating 11 families of LLMs on them.

Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real physical world. This paper delves into these questions by exploring how LLMs can be extended to interact with and reason about the physical world through IoT sensors and actuators, a concept that we term “Penetrative AI”. The paper explores such an extension at two levels of LLMs’ ability to penetrate into the physical world via the processing of sensory signals. Our preliminary findings indicate that LLMs, with ChatGPT being the representative example in our exploration, have considerable and unique proficiency in employing the embedded world knowledge for interpreting IoT sensor data and reasoning over them about tasks in the physical realm. Not only this opens up new applications for LLMs beyond traditional text-based tasks, but also enables new ways of incorporating human knowledge in cyber-physical systems.

With the enhancement in the field of generative artificial intelligence (AI), contextual question answering has become extremely relevant. Attributing model generations to the input source document is essential to ensure trustworthiness and reliability. We observe that when large language models (LLMs) are used for contextual question answering, the output answer often consists of text copied verbatim from the input prompt which is linked together with “glue text” generated by the LLM. Motivated by this, we propose that LLMs have an inherent awareness from where the text was copied, likely captured in the hidden states of the LLM. We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of LLMs. Our approach bypasses the need for extensive model retraining and retrieval model overhead, offering granular attributions and preserving the quality of generated answers. Our experimental results demonstrate that our method performs on par or better than GPT-4 at identifying verbatim copied segments in LLM generations and in attributing these segments to their source. Importantly, our method shows robust performance across various LLM architectures, highlighting its broad applicability. Additionally, we present Verifiability-granular, an attribution dataset which has token level annotations for LLM generations in the contextual question answering setup.

Conversational recommender systems are an emerging area that has garnered increasing interest in the community, especially with the advancements in large language models (LLMs) that enable sophisticated handling of conversational input. Despite the progress, the field still has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that PEARL contains more specific user preferences, show expertise in the target domain, and provides recommendations more relevant to the dialogue context than those in prior datasets. Furthermore, we demonstrate the utility of PEARL by showing that our downstream models outperform baselines in both human and automatic evaluations. We release our dataset and code.

Multimodal large language models (MLLMs) have proven effective in a wide range of tasks that require complex reasoning and linguistic comprehension. However, due to a lack of high-quality multimodal resources in languages other than English, the success of MLLMs remains relatively limited to English-based settings. This poses significant challenges in developing comparable models for other languages, even those with large speaker populations, such as Arabic. To alleviate this challenge, we introduce a comprehensive family of Arabic MLLMs, dubbed *Peacock*, with strong vision and language capabilities. Through comprehensive qualitative and quantitative analysis, we demonstrate the solid performance of our models on various visual reasoning tasks and further show their emerging dialectal potential. Additionally, we introduce *Henna*, a new benchmark specifically designed for assessing MLLMs on aspects related to Arabic culture, setting the first stone for culturally-aware Arabic MLLMs. The GitHub repository for the *Peacock* project is available at [https://github.com/UBC-NLP/peacock](https://github.com/UBC-NLP/peacock).

Large language models (LLMs) have showcased their remarkable capabilities to handle various downstream tasks, including multilingual machine translation ability. Despite their impressive performance, decoder-only LLMs lack an explicit alignment between source and target contexts, leading to translation that may not faithfully represent the original content. To address this, we propose three learning strategies to encourage LLMs to pay more attention to the source context during translation: 1) adjusting attention weights on the source context by adaptive attention re-weighting; 2) suppressing the irrelevant target prefix using contrastive decoding; 3) avoiding excessive reliance on the target prefix through target-constrained tuning. To verify the effectiveness of our model, we curate a new dataset specifically focusing on unfaithful translations generated by LLMs. Experimental results on both human-collected and general test sets verify the effectiveness of our model across multiple language pairs. Further human evaluation demonstrates the efficacy of our method in reducing hallucinatory translation and improving the fidelity of translations.