China

Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also be used to efficiently uncover scaling laws that guide the building of LLMs to achieve a desirable performance with significantly better cost-effectiveness.

EMNLP 2025

Uncovering Scaling Laws for Large Language Models via Inverse Problems

cost-effectiveness

scaling laws

inverse problems

large language models

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Determining faithfulness of a claim to a source document is an important problem across many domains. This task is generally treated as a binary judgment of whether the claim is supported or unsupported in relation to the source. In many cases, though, whether a claim is supported can be ambiguous. For instance, it may depend on making inferences from given evidence, and different people can reasonably interpret the claim as either supported or unsupported based on their agreement with those inferences. Forcing binary labels upon such claims lowers the reliability of evaluation. In this work, we reframe the task to manage the subjectivity involved with factuality judgments of ambiguous claims. We introduce LLM-generated edits of summaries as a method of providing a nuanced evaluation of claims: how much does a summary need to be edited to be unambiguous? Whether a claim gets rewritten and how much it changes can be used as an automatic evaluation metric, the Ambiguity Rewrite Metric (ARM), with a much richer feedback signal than a binary judgment of faithfulness. We focus on the area of narrative summarization as it is particularly rife with ambiguity and subjective interpretation. We show that ARM produces a 21% absolute improvement in annotator agreement on claim faithfulness, indicating that subjectivity is reduced.

Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding

The growing influence of video content as a medium for communication and misinformation underscores the urgent need for effective tools to analyze claims in multilingual and multi-topic settings. Existing efforts in misinformation detection largely focus on written text, leaving a significant gap in addressing the complexity of spoken text in video transcripts. We introduce ViClaim, a dataset of 1,798 annotated video transcripts across three languages (English, German, Spanish) and six topics. Each sentence in the transcripts is labeled with three claim-related categories: fact-check-worthy, fact-non-check-worthy, or opinion. We developed a custom annotation tool to facilitate the highly complex annotation process. Experiments with state-of-the-art multilingual language models demonstrate strong performance in cross-validation (macro F1 up to 0.896) but reveal challenges in generalization to unseen topics, particularly for distinct domains. Our findings highlight the complexity of claim detection in video transcripts. ViClaim offers a robust foundation for advancing misinformation detection in video-based communication, addressing a critical gap in multimodal analysis.

ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos

Knowledge Graph (KG) reasoning, which aims to infer new facts from structured knowledge repositories, plays a vital role in Natural Language Processing (NLP) systems. Its effectiveness critically depends on constructing informative and contextually relevant reasoning paths. However, existing graph neural networks (GNNs) often adopt rigid, query-agnostic path-exploration strategies, limiting their ability to adapt to diverse linguistic contexts and semantic nuances. To address these limitations, we propose \textbf{MoKGR}, a mixture-of-experts framework that personalizes path exploration through two complementary components: (1) a mixture of length experts that adaptively selects and weights candidate path lengths according to query complexity, providing query-specific reasoning depth; and (2) a mixture of pruning experts that evaluates candidate paths from a complementary perspective, retaining the most informative paths for each query. Through comprehensive experiments on diverse benchmark, MoKGR demonstrates superior performance in both transductive and inductive settings, validating the effectiveness of personalized path exploration in KGs reasoning.

Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning

While large language models have shown reasoning capabilities, their application to the audio modality, particularly in large audio-language models (ALMs), remains significantly underdeveloped. Addressing this gap requires a systematic approach, involving a capable base model, high-quality reasoning-oriented audio data, and effective training algorithms. In this study, we present a comprehensive solution: we introduce the Audio Logical Reasoning (ALR) dataset, consisting of 6,446 text-audio annotated examples specifically designed for complex reasoning tasks. Building on this resource, we propose SoundMind, a rule-based reinforcement learning (RL) algorithm tailored to endow ALMs with deep bimodal reasoning abilities. By training Qwen2.5-Omni-7B on the ALR dataset using SoundMind, our approach achieves state-of-the-art performance in audio logical reasoning. This work highlights the impact of combining high-quality, reasoning-focused datasets with specialized RL techniques, advancing the frontier of auditory intelligence in language models. Code: https://anonymous.4open.science/r/SoundMind-RL.

SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models

We present SportReason, a benchmark for retrieval-augmented reasoning on numerical sports questions. Unlike existing benchmarks limited to one or two evidence units, SportReason requires combining and reasoning across free-text, structured tables, and semi-structured infoboxes. We provide 3,000 human-verified QA pairs by repurposing existing QA and table generation datasets, and by prompting large language models (LLMs). Each pair is grounded in multiple evidence from a multi-modal Wikipedia corpus containing 200K knowledge contexts. We evaluate existing retrievers and rerankers, along with agentic Retrieval-Augmented Generation (RAG) systems. The experimental results show that multi-evidence retrieval remains a challenge. Agentic RAG systems (e.g., Search-o1), despite iterative retrieval and reasoning capabilities, fail to improve performance due to imprecise queries, simple training, and distracting information.

SportReason: Evaluating Retrieval-Augmented Reasoning across Tables and Text for Sports Question Answering

Tool usage is a proven technique for developing high-performance reasoning in large language models (LLMs). Our work is focused on emphasizing the utility of leveraging multiple diverse tools for complex reasoning tasks. We present textbfMulti-TAG, a textbfMulti-textbfTool textbfAGgregation-based LLM framework that utilizes multiple diverse tools to solve complex math problems over multiple reasoning steps. At each reasoning step, textbfMulti-TAG invokes multiple tools and accepts the solution of the respective step by tools that have majority agreement on the final answer estimate. textbfMulti-TAG strongly outperforms several standard baselines that use individual tools with the same number of runs, highlighting the importance of multi-tool invocation for solving complex reasoning tasks. We also show that naive aggregation of multiple tools at each reasoning step also leads to substantial improvements of up to 35% accuracy. textbfMulti-TAG then further improves these gains by 7.4% on average on MATH500, AIME, AMC, and OlympiadBench.

Diverse Multi-tool Aggregation with Large Language Models for Enhanced Math Reasoning

Large Reasoning Models (LRMs) have recently gained attention for their strong reasoning capabilities in solving complex problems. Reflective reasoning—revisiting and refining prior thoughts—has been shown to significantly enhance performance. However, excessive reflection can lead to longer outputs, increased inference time and cost, and a higher risk of hallucination. Existing training methods rarely address this trade-off effectively. We propose ReFLAIR, a unified framework for reflective reasoning guided by structured [object Object][object Object][object Object] trajectories and hybrid rewards. First, we construct a high-quality ReFLAIR-cold dataset comprising 30K diverse multimodal reasoning samples annotated with introspective revisions. Then, we train a Reflection Quality Scorer (RQS) to assess the value added by each re-thinking step. Finally, we adopt a modified GRPO-based reinforcement learning approach, which combines answer accuracy, structural compliance, reflection utility, and task difficulty into a hybrid reward for policy optimization. ReFLAIR achieves up to +12.2% accuracy improvement on challenging benchmarks including MathVista, MM-Math, and so on, while consistently enhancing reflection quality and reasoning robustness. Our findings demonstrate that structured reflection offers a scalable and generalizable pathway to improving step-by-step reasoning in multimodal LLMs.

ReFLAIR: Enhancing Multimodal Reasoning via Structured Reflection and Reward-Guided Learning

Negations are key to determining sentence meaning, making them essential for logical reasoning. Despite their importance, negations pose a substantial challenge for large language models (LLMs) and remain underexplored. We constructed and published two new textual entailment datasets NoFEVER-ML and NoSNLI-ML in four languages (English, Czech, German, and Ukrainian) with textitpaired examples differing in negation. It allows investigation of the root causes of the negation problem and its exemplification: how popular LLM model properties and language impact their inability to handle negation correctly. Contrary to previous work, we show that increasing the model size may improve the models' ability to handle negations. Furthermore, we find that both the models' reasoning accuracy and robustness to negation are language-dependent and that the length and explicitness of the premise have an impact on robustness. There is better accuracy in projective language with fixed order, such as English, than in non-projective ones, such as German or Czech. Our entailment datasets pave the way to further research for explanation and exemplification of the negation problem, minimization of LLM hallucinations, and improvement of LLM reasoning in multilingual settings.

Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis

Inference constitutes the majority of costs throughout the lifecycle of a large language model (LLM). While numerous LLM inference engines focusing primarily on low-level optimizations have been developed, there is a scarcity of non-intrusive client-side frameworks that perform high-level optimizations. In this paper, we introduce CacheSaver, a modular, plug-and-play, and asynchronous framework that facilitates high-level inference optimizations, thereby integrating cleanly into existing systems without requiring changes to the end-user application logic or the underlying LLM. The key novelty is a namespace-aware list-valued cache that ensures statistical integrity of LLM responses by generating independent and identically distributed responses within a namespace as well as ensuring reproducibility. Moreover, as a direct consequence of operating at a high level, CacheSaver supports both local and online models. We conduct extensive experiments with five representative state-of-the-art reasoning strategies, five diverse benchmark tasks, and three different LLMs. On average across all methods, tasks, and LLMs, CacheSaver reduces cost by approximately 25% and CO2 emissions by approximately 35%. Notably, CacheSaver excels in practical machine learning scenarios such as benchmarking across multiple methods or conducting ablation analysis of a specific method, obtaining substantial cost and carbon footprint reduction of approximately 60%. CacheSaver is publicly available at https://anonymous.4open.science/r/cachesaver-7060.

Cache Saver: A Modular Framework for Efficient, Affordable, and Reproducible LLM Inference

Allusion recognition—a task demanding contextual activation of cultural knowledge—serves as a critical test of large language models’ (LLMs) ability to deploy stored information in open-ended, figurative settings. We introduce a framework for evaluating Persian literary allusions through (1) classical poetry annotations and (2) LLM-generated texts embedding allusions in novel contexts. By combining knowledge assessments, multiple-choice tasks, and open-ended recognition, we isolate whether failures stem from knowledge gaps or activation challenges. Evaluations across 11 LLMs reveal a critical disconnect: while models exhibit strong foundational knowledge and high multiple-choice accuracy, their performance drops significantly in open-ended settings, particularly for indirect references. Reasoning-optimized models generalize better to novel contexts, whereas distilled models show marked degradation in cultural reasoning. The gap underscores that LLMs’ limitations arise not from missing knowledge but contextual recall failure—an inability to spontaneously activate cultural references without explicit cues. Our work positions allusion recognition as a benchmark for evaluating contextual knowledge deployment, urging training paradigms that bridge factual recall and culturally grounded reasoning.

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