China

As Large Language Models (LLMs) continue to scale, understanding how effectively their internal capacity is utilized becomes increasingly important, especially for inference-time efficiency. While existing scaling laws relate model size to loss and compute, they offer little insight into the representational dynamics of individual components. In this work, we focus on the Feed-Forward Network (FFN), a dominant sub-block in decoder-only transformers, and recast FFN width selection as a \emph{spectral utilization} problem. We introduce a lightweight, differentiable diagnostic suite comprising: \textbf{Hard Rank} (Participation Ratio), Soft Rank (spectral entropy), Spectral Concentration, and the composite Spectral Utilization Index (SUI), designed to quantify how many latent directions are meaningfully activated. Our spectral audit across GPT-2, LLaMA, and nGPT models reveals that spectral utilization grows with model size but not monotonically with width, often peaking at intermediate dimensions (e.g., D=2048). We identify clear instances of \emph{spectral collapse}, where wider FFNs concentrate variance into a narrow subspace, leaving much of the latent space unused.

EMNLP 2025

Spectral Scaling Laws in Language Models: emphHow Effectively Do Feed-Forward Networks Use Their Latent Space?

eigenspectrum

spectral rank

feed-forward networks

scaling laws

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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.

Although reasoning has become a central focus for large language models, there remains a significant gap in corpora and human evaluation methods for lower-resource languages. To address this, we introduce the Trojsten Benchmark -- a dataset of 1,108 high-school competition problems in mathematics, physics, and programming sourced from Slovak archives -- that challenges conventional grading due to its complex formatting and language. We further introduce a LLM-based rubric-based grading approach to generate detailed evaluation rubrics and grade open-ended solutions, with our experiments revealing an average absolute grading difference of just 1.05 points compared to human evaluators. Extensive experiments benchmark various prompting techniques and models -- including GPT-3.5-Turbo, GPT-4, GPT-4o, and open-weight models like Llama 3 -- highlighting both promising performance and persistent challenges in multistep reasoning assessment.

Trojsten Benchmark: Evaluating LLM Problem-Solving in Slovak STEM Competition Problems

Large language models (LLMs) excel at many NLP tasks, yet their multi-step logical reasoning remains unreliable. Existing solutions such as Chain-of-Thought prompting generate intermediate steps but provide no internal check of their logical coherence. In this paper, we use the ``QK-score'', a lightweight metric based on query–key alignments within transformer attention heads, to evaluate the logical reasoning capabilities of LLMs. Our method automatically identifies attention heads that play a key role in distinguishing valid from invalid logical inferences, enabling efficient inference-time evaluation via a single forward pass. It reveals latent reasoning structure in LLMs and provides a scalable mechanistic alternative to ablation-based analysis. Across three benchmarks: ProntoQA-OOD, PARARULE-Plus, and MultiLogicEval, with models ranging from 1.5B to 70B parameters, the selected heads predict logical validity up to 14% better than the model probabilities, and remain robust under distractors and increasing reasoning depth of dleq 6.

Quantifying Logical Consistency in Transformers via Query-Key Alignment

Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. Since human studies are costly, time-consuming, and hard to reproduce, recent work explores using LLMs to simulate users for automatic assistant evaluation. However, there is no benchmark or systematic study to evaluate whether these simulated users are reliable stand-ins for real users. To address this, we introduce USimBench, a benchmark of 909 annotated human–LLM conversations on two interactive tasks---math tutoring and document creation. USimBench evaluates simulators based on how closely their messages match human behavior and how well their assistant ratings align with human judgments. Experiments on various simulator methods show that simulators conditioned on user profiles, capturing traits like background and message styles, align closely with human judgments. They reach Spearman's ρ of 0.7 on both tasks, providing a practical, scalable alternative to human evaluation.

SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants?

Methods for lexical semantic change detection quantify changes in the meaning of words over time. Prior research produced many methods that excel when evaluated on established benchmarks that consist of annotated sets of pre-selected \emph{target} words; the targets are chosen ahead of time due to the prohibitively large cost of manually annotating all words. However, performance measured on small curated sets of words cannot reveal how well these methods would perform when applied to \emph{discovering} semantic changes among the \emph{full} corpus vocabulary, which is the actual end goal for many applications. In this paper, we implement a top-k set-up to evaluate semantic change discovery (SCDisc) despite lacking complete annotations (although a contribution of our efforts is a 50% extension of the annotations in the commonly used LiverpoolFC benchmark). We use our evaluation setup to investigate a battery of semantic change detection methods that utilize three different kinds of contextualized representations, as well as recent techniques specifically designed to reduce false-discovery rates, such as statistical significance tests and metric scaling. We find that despite great performance on pre-existing benchmarks, \emph{all} methods of semantic change detection struggle at ranking known changes higher compared to other words in the vocabulary when presented with a \emph{natural} distribution of instances. Finally, we verify that the majority of words with high detected change scores do not in fact experience meaning changes. In fact, for most of the methods less than a third of highest ranked changes were annotated to have changed in meaning. Given the large performance discrepancies between semantic-change scoring and discovery ``in the wild'', we recommend that researchers direct more attention to semantic change discovery and include it in their suite of evaluations when proposing new methods.

Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild

In this paper, we introduce Spotlight, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a document. Unlike traditional summaries, which prioritize comprehensive coverage, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. We formally differentiate spotlights from related constructs and support our analysis with a detailed benchmarking study using new datasets curated for this work. To generate high-quality spotlights, we propose a two-stage approach: fine-tuning a large language model on our benchmark data, followed by alignment via Direct Preference Optimization (DPO). Our comprehensive evaluation demonstrates that the resulting model not only identifies key elements with precision but also enhances readability and boosts the engagement value of the original document.

Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents

Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining (AM). This paper investigates the integration of state-of-the-art LLMs into ArgSum, including for its evaluation. In particular, we propose a novel prompt-based evaluation scheme, and validate it through a novel human benchmark dataset. Our work makes three main contributions: (i) the integration of LLMs into existing ArgSum frameworks, (ii) the development of a new LLM-based ArgSum system, benchmarked against prior methods, and (iii) the introduction of an advanced LLM-based evaluation scheme. We demonstrate that the use of LLMs substantially improves both the generation and evaluation of argument summaries, achieving state-of-the-art results and advancing the field of ArgSum. We also show that among the four LLMs integrated in (i) and (ii), Qwen-3-32B, despite having the fewest parameters, performs best, even surpassing GPT-4o, while LLaMA-3.3-70B consistently underperforms.

Argument Summarization and its Evaluation in the Era of Large Language Models

This work investigates capturing and modeling disagreement in Semantic Textual Similarity (STS), where sentence pairs are assigned ordinal similarity labels (0–5). Conventional STS systems average multiple annotator scores and focus on a single numeric estimate, overlooking label dispersion. By leveraging the disaggregated SemEval-2015 dataset (Soft-STS-15), the authors propose a disagreement-aware calibration strategy that treats STS as an ordinal distribution prediction problem. Using a cross-encoder trained with a distance-aware objective, they produce softer, probabilistic output and apply post-hoc temperature scaling to refine calibration. Results show improved performance in distance-based metrics and robust handling of ambiguous pairs, demonstrating that modeling disagreement benefits both calibration and ranking accuracy. The paper thus highlights the value of retaining and modeling full annotation distributions, rather than collapsing them to a single mean label.

Beyond Averages: Learning with Annotator Disagreement in STS

Topic models often fail to capture low-prevalence, domain-critical themes—so-called minority topics—such as mental health themes in online comments. While some existing methods can incorporate domain knowledge such as expected topical content, methods allowing guidance may require overly detailed expected topics, hindering the discovery of topic divisions and variation. We propose a topic modeling solution via a specially constrained NMF. We incorporate a seed word list characterizing minority content of interest, but we do not require experts to pre-specify their division across minority topics. Through prevalence constraints on minority topics and seed word content across topics, we learn distinct data-driven minority topics as well as majority topics. The constrained NMF is fitted via Karush-Kuhn-Tucker (KKT) conditions with multiplicative updates. We outperform several baselines on synthetic data in terms of topic purity, normalized mutual information, and also evaluate topic quality using Jensen-Shannon divergence (JSD). We conduct a case study on YouTube vlog comments, analyzing viewer discussion of mental health content; our model successfully identifies and reveals this domain relevant minority content.

Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics

Whether language models (LMs) have inductive biases that favor typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs) (White and Cotterell, 2021; Kuribayashi et al., 2024). In this paper, we extend these works from two perspectives. First, we extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) (Wood, 2014), which allows ALs to cover attested but previously overlooked constructions, such as unbounded dependency and mildly context-sensitive structures. Second, our evaluation focuses more on the generalization ability of LMs to process longer sentences, while previous studies did not. These lead to better extensibility of ALs and clearer conclusions than in the in-domain evaluation --- typologically plausible word orders tend to be easier for LMs to productively generalize.

Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages

Interpretability is a key challenge in fostering trust for Large Language Models (LLMs), which stems from the complexity of extracting reasoning from model's parameters. We present the Frame Representation Hypothesis, a theoretically robust framework grounded in the Linear Representation Hypothesis (LRH) to interpret and control LLMs by modeling multi-token words. Prior research explored LRH to connect LLM representations with linguistic concepts, but was limited to single token analysis. As most words are composed of several tokens, we extend LRH to multi-token words, thereby enabling usage on any textual data with thousands of concepts. To this end, we propose words can be interpreted as frames, ordered sequences of vectors that better capture token-word relationships. Then, concepts can be represented as the average of word frames sharing a common concept. We showcase these tools through Top-k Concept-Guided Decoding, which can intuitively steer text generation using concepts of choice. We verify said ideas on Llama 3, Gemma 2, Phi 3 and Qwen-2-VL families, demonstrating gender and language biases, exposing harmful content, but also potential to remediate them, leading to safer and more transparent LLMs. Code is available at https://github.com/phvv-me/frame-representation-hypothesis.git

Downloads

Next from EMNLP 2025

Trojsten Benchmark: Evaluating LLM Problem-Solving in Slovak STEM Competition Problems

Similar lecture

Spectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization