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Large Language Models (LLMs) rely on generating extensive intermediate reasoning units (e.g., tokens, sentences) to enhance final answer quality across a wide range of complex tasks. While this approach has proven effective, it inevitably increases substantial inference costs. Previous methods adopting token-level reduction without clear criteria result in poor performance compared to models trained with complete rationale. To address this challenge, we propose a novel sentence-level rationale reduction framework leveraging likelihood-based criteria, *verbosity*, to identify and remove redundant reasoning sentences. Unlike previous approaches, our method leverages *verbosity* to selectively remove redundant reasoning sentences while preserving reasoning capabilities. Our experimental results across various reasoning tasks demonstrate that our method improves performance by an average of 7.71% while reducing token generation by 19.87% compared to model trained with complete reasoning paths.

ACL 2025

Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning

efficient and effective reasoning

reasoning path reduction

reasoning

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Retrieval-Augmented Generation (RAG) is a powerful strategy for improving the factual accuracy of models by retrieving external knowledge relevant to queries and incorporating it into the generation process. However, existing approaches primarily focus on text, with some recent advancements considering images, and they largely overlook videos, a rich source of multimodal knowledge capable of representing contextual details more effectively than any other modality. While very recent studies explore the use of videos in response generation, they either predefine query-associated videos without retrieval or convert videos into textual descriptions losing multimodal richness. To tackle these, we introduce VideoRAG, a framework that not only dynamically retrieves videos based on their relevance with queries and but also utilizes both visual and textual information. The operation of VideoRAG is powered by recent Large Video Language Models (LVLMs), which enable the direct processing of video content to represent it for retrieval and the seamless integration of retrieved videos jointly with queries for response generation. Also, inspired by that the context size of LVLMs may not be sufficient to process all frames in extremely long videos and not all frames are equally important, we introduce a video frame selection mechanism to extract the most informative subset of frames, along with a strategy to extract textual information from videos (as it can aid the understanding of video content) when their subtitles are not available. We experimentally validate the effectiveness of VideoRAG, showcasing that it is superior to relevant baselines.

VideoRAG: Retrieval-Augmented Generation over Video Corpus

Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large language model. Humans solve this problem by also considering the different facets that define a task (e.g., counter-examples, explanations, analogies) and including them in the prompt. However, it is unclear whether existing algorithmic approaches, based on iteratively editing a given prompt or automatically selecting a few in-context examples, can cover the multiple facets required to solve a complex task. In this work, we view prompt optimization as that of learning multiple facets of a task from a set of training examples. We exploit structure in the prompt optimization problem and break down a prompt into loosely coupled semantic sections. The proposed algorithm, UniPrompt, (1) clusters the input space and uses clustered batches so that each batch likely corresponds to a different facet of the task, and (2) utilizes a feedback mechanism to propose adding, editing or deleting a section, which in turn is aggregated over a batch to capture generalizable facets. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using UniPrompt obtain higher accuracy than human-tuned prompts and those from state-of-the-art methods. In particular, our algorithm can generate long, complex prompts that existing methods are unable to generate.

Task Facet Learning: A Structured Approach To Prompt Optimization

To enhance reasoning capabilities, research has explored incorporating special-purpose tokens into the training process to strengthen the learning mechanism of transformer-based LLMs. Building on prior research that showed inserting dummy tokens consecutively just before reasoning steps can enhance effectiveness, we introduce a novel approach termed Dynamic Inserting tokens Training (DIT). Our method identifies positions within sequences where model confidence is lowest identified by token log likelihood, strategically inserting [PAUSE] tokens to bolster the model's predictive capabilities for subsequent tokens. Experimental results across diverse datasets and models, from 2.7B model to 8B model, demonstrate that DIT consistently outperforms traditional fine-tuning and previous token insertion methods. With this simple yet impactful method, we achieve accuracy gains of up to 4.7%p on GSM8K, 3.23%p on AQUA-RAT, and pass@1 improvements of up to 3.4%p on MBPP. Our work shows a model-based, dynamic approach rather than a heuristic one, thereby broadening the scope of research in reasoning.

Learning to Insert [PAUSE] Tokens for Better Reasoning

Many applications of Large Language Models (LLMs) require them to either simulate people or offer personalized functionality, making the demographic representativeness of LLMs crucial for equitable utility. At the same time, we know little about the extent to which these models actually reflect the demographic attributes and behaviors of certain groups or populations in valid ways. Empirical research is divided, with conflicting findings on whether LLMs can accurately represent human (sub)populations. To shed light on this debate, we review 211 papers on the demographic representativeness of LLMs. We find that while 29% of the studies report positive conclusions, 30% of these do not evaluate LLMs across multiple demographic categories or within demographic subcategories. Another 35-47% fail to specify these subcategories altogether for gender and race. Out of the papers that do report subcategories, a majority do not include results on marginalized groups in their study. This suggests an inflated perception of LLM representativeness in the broader community. We recommend more precise evaluation methods and comprehensive documentation to ensure the responsible use of LLMs for social applications.

Do Large Language Models Represent the People? A Systematic Literature Review on the Demographic Representativeness of Large Language Models

Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In this paper, we introduce Mosaic Instruction Tuning (Mosaic-IT), a human/model-free compositional data synthesis method that can efficiently create rich and diverse augmentations from existing instruction tuning data to enhance the LLMs. Mosaic-IT randomly concatenates multiple instruction data into one and trains the model to produce the corresponding responses with predefined higher-level meta-instructions to strengthen its multi-step instruction-following and format-following skills. Our extensive evaluations demonstrate a superior performance and training efficiency of Mosaic-IT, which achieves consistent performance improvements over various benchmarks and a 
reduction in training costs compared with the original instruction tuning.

Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning

Natural language processing evaluation has made significant progress, largely driven by the proliferation of powerful large language mod-els (LLMs). New evaluation benchmarks are of increasing priority as the reasoning capabilities of LLMs are expanding at a rapid pace. In particular, while multi-document (MD) reasoning is an area of extreme relevance given LLM capabilities in handling longer-context inputs, few benchmarks exist to rigorously examine model behavior in this setting. Moreover, the multi-document setting is historically challenging for benchmark creation due to the expensive cost of annotating long inputs. In this work, we introduce MDBench, a new dataset for evaluating LLMs on the task of multi-document reasoning. Notably, MDBench is created through a novel synthetic generation process, allowing us to controllably and efficiently generate challenging document sets and the corresponding question-answer (QA) examples. Our novel technique operates on condensed structured seed knowledge, modifying it through LLM-assisted edits to induce MD-specific reasoning challenges. We then convert this structured knowledge into a natural text surface form, generating a document set and corresponding QA example. We analyze the behavior of popular LLMs and prompting techniques, finding that MDBench poses significant challenges for all methods, even with relatively short document sets. We also see our knowledge-guided generation technique (1) allows us to readily perform targeted analysis of MD-specific reasoning capabilities and (2) can be adapted quickly to account for new challenges and future modeling improvements.

MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance

Speculative Decoding (SD) enforces strict distributional equivalence to the target model, limiting potential speed ups as distributions of near-equivalence achieve comparable outcomes in many cases. Furthermore, enforcing distributional equivalence means that users are unable to trade deviations from the target model distribution for further inference speed gains. To address these limitations, we introduce Fuzzy Speculative Decoding (FSD) - a decoding algorithm that generalizes SD by accepting candidate tokens purely based on the divergences between the target and draft model distributions. By allowing for controlled divergence from the target model, FSD enables users to flexibly trade generation quality for inference speed. Across several benchmarks, our method is able to achieve significant runtime improvements of over 5 tokens per second faster than SD at only an approximate 2% absolute reduction in benchmark accuracy. In many cases, FSD is even able to match SD benchmark accuracy at over 2 tokens per second faster, demonstrating that distributional equivalence is not necessary to maintain target model performance.

Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff

Recent advancements in large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, such as math problem-solving and code generation. However, multi-hop question answering (MHQA) over long contexts, which demands both robust knowledge-intensive reasoning and efficient processing of lengthy documents, remains a significant challenge. Existing approaches often struggle to balance these requirements, either neglecting explicit reasoning or incurring expensive computational costs due to full-attention mechanisms over long contexts. To address this, we propose **Search-in-Context (SIC)**, a novel framework that integrates Monte Carlo Tree Search (MCTS) with dynamic key-value (KV) retrieval to enable iterative, context-aware reasoning. SIC dynamically retrieves critical KV pairs (e.g., 4K tokens) at each step, prioritizing relevant evidence while mitigating the "lost in the middle" problem. Furthermore, the paper introduces a Process-Reward Model (PRM) trained on auto-labeled data to guide the MCTS process with stepwise rewards, promoting high-quality reasoning trajectories without manual annotation. Experiments on three long-context MHQA benchmarks (HotpotQA, 2WikiMultihopQA, MuSiQue) and a counterfactual multi-hop dataset demonstrate SIC’s superiority, achieving state-of-the-art performance while significantly reducing computational overhead.

Search-in-Context: Efficient Multi-Hop QA over Long Contexts via Monte Carlo Tree Search with Dynamic KV Retrieval

Knowledge-grounded question answering (QA) leverages knowledge graphs (KGs) to provide answers based on structured factual data. Existing approaches are mainly divided into Knowledge Graph Question Answering (KGQA) and Complex Query Answering (CQA). Both approaches have limitations: the first struggles when essential triplets related to the questions are missing in the given KG, while the second depends on structured first-order logic queries. To overcome these limitations, we propose a novel framework, termed Aqua-QA. Aqua-QA approximates query graphs from natural language questions, enabling reasoning over KGs. We evaluate Aqua-QA on challenging QA tasks where the entities or relations representing the input questions are excluded from the KGs, and complex logical reasoning is required to answer natural language questions. Experimental results on these datasets demonstrate that Aqua-QA outperforms existing methods, showcasing its effectiveness in handling complex reasoning tasks in knowledge-grounded QA settings.

Enhancing Complex Reasoning in Knowledge Graph Question Answering Through Query Graph Approximation

In this reproduction study, we revisit recent claims that self-attention implements kernel principal component analysis (KPCA) (Teo and Nguyen, 2024), positing that (i) value vectors $V$ capture the eigenvectors of the Gram matrix of the keys, and (ii) that self-attention projects queries onto the principal component axes of the key matrix $K$ in a feature space. Our analysis reveals three critical inconsistencies: (1) No alignment exists between learned self-attention value vectors and what is proposed in the KPCA perspective, with average similarity metrics (optimal cosine similarity $\leq 0.32$, linear CKA (Centered Kernel Alignment) $\leq 0.11$, kernel CKA $\leq 0.32$) indicating negligible correspondence; (2) Reported decreases in reconstruction loss $J_\text{proj}$, arguably justifying the claim that the self-attention
minimizes the projection error of KPCA, are misinterpreted, as the quantities involved differ by orders of magnitude ($\sim 10^3$); (3) Gram matrix eigenvalue statistics, introduced to justify that $V$ captures the eigenvector of the gram matrix, are irreproducible without undocumented implementation-specific adjustments. Across 10 transformer architectures, we conclude that the KPCA interpretation of self-attention lacks empirical support.

Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning

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VideoRAG: Retrieval-Augmented Generation over Video Corpus

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