ACL 2025 论文列表

Prompt compression is important for large language models (LLMs) to increase inference speed, reduce costs, and improve user experience. However, current methods face challenges such as low compression ratios and potential training-test overlap during evaluation. To address these issues, we propose 500xCompressor, a method that compresses natural language contexts into a minimum of one special token and demonstrates strong generalization ability. The 500xCompressor introduces approximately 0.3% additional parameters and achieves compression ratios ranging from 6x to 500x, achieving 27-90% reduction in calculations and 55-83% memory savings when generating 100-400 tokens for new and reused prompts at 500x compression, while retaining 70-74% (F1) and 77-84% (Exact Match) of the LLM capabilities compared to using non-compressed prompts. It is designed to compress any text, answer various types of questions, and can be utilized by the original LLM without requiring fine-tuning. Initially, 500xCompressor was pretrained on the ArxivCorpus, followed by fine-tuning on the ArxivQA dataset, and subsequently evaluated on strictly unseen and cross-domain question answering (QA) datasets. This study shows that KV values outperform embeddings in preserving information at high compression ratios. The highly compressive nature of natural language prompts, even for detailed information, suggests potential for future applications and the development of a new LLM language.

The rapid advancement of Multi-modal Language Models (MLLMs) has significantly enhanced performance in multimodal tasks, yet these models often exhibit inherent biases that compromise their reliability and fairness. Traditional debiasing methods face a trade-off between the need for extensive labeled datasets and high computational costs. Model merging, which efficiently combines multiple models into a single one, offers a promising alternative but its usage is limited to MLLMs with the same architecture. We propose 3DM, a novel framework integrating Distill, Dynamic Drop, and Merge to address these challenges. 3DM employs knowledge distillation to harmonize models with divergent architectures and introduces a dynamic dropping strategy that assigns parameter-specific drop rates based on their contributions to bias and overall performance. This approach preserves critical weights while mitigating biases, as validated on the MMSD2.0 sarcasm detection dataset. Our key contributions include architecture-agnostic merging, dynamic dropping, and the introduction of the Bias Ratio (BR) metric for systematic bias assessment. Empirical results demonstrate that 3DM outperforms existing methods in balancing debiasing and enhancing the overall performance, offering a practical and scalable solution for deploying fair and efficient MLLMs in real-world applications.

We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 91 spoken languages at the intersection of BELEBELE and FLEURS, and one sign language (ASL). As a by-product we also extend the Automatic Speech Recognition Benchmark, FLEURS, by 20%. We evaluate 2M-BELEBELE dataset for both 5-shot and zero-shot settings and across languages, the speech comprehension accuracy is ≈ 10% average lower compared to reading comprehension.

Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM shall enable its users to effortlessly process many originally exhausting tasks — e.g., digesting a long-form document to find answers v.s., directly asking an LLM about it. However, existing real-task-based long-context evaluation benchmarks have a few major shortcomings. For instance, some Needle-in-a-Haystack-like benchmarks are too synthetic, and therefore do not represent the real world usage of LLMs. While some real-task-based benchmarks like LongBench avoid this problem, such benchmarks are often formed in a way where each data sample has a fixed sequence length, which not only makes them solely suitable for models with a certain range of context windows, but also lacks a proxy to know at what length the model/method-of-interest would fail. Last, most benchmarks tend to not provide proper metrics to separate long-context performance from the model’s baseline ability, so when conducting a cross-model/recipe comparison, such conflation makes the user unable to understand how exactly one model or recipe excels at the long-context task in relation to its baseline ability. To address these issues, we introduce a length-controllable, real-life reflective benchmark with a novel metric that disentangles baseline knowledge from long-context capabilities. Experiments demonstrate the superiority of our datasets in effectively evaluating LLMs. All assets are available at https://github.com/uservan/100-LongBench.git.

Figurative language (e.g., irony, hyperbole, understatement) is ubiquitous in human communication, resulting in utterances where the literal and the intended meanings do not match. The Rational Speech Act (RSA) framework, which explicitly models speaker intentions, is the most widespread theory of probabilistic pragmatics, but existing implementations are either unable to account for figurative expressions or require modeling the implicit motivations for using figurative language (e.g., to express joy or annoyance) in a setting-specific way. In this paper, we introduce the Rhetorical-Strategy-Aware RSA (RSA)² framework which models figurative language use by considering a speaker’s employed rhetorical strategy. We show that (RSA)² enables human-compatible interpretations of non-literal utterances without modeling a speaker’s motivations for being non-literal. Combined with LLMs, it achieves state-of-the-art performance on the ironic split of PragMega+, a new irony interpretation dataset introduced in this study.