United States

Language models perform well on grammatical agreement, but it is unclear whether this reflects rule-based generalization or memorization. We study this question for German definite singular articles, whose forms depend on gender and case. Using GRADIEND, a gradient-based interpretability method, we learn parameter update directions for gender-case specific article transitions. We find that updates learned for a specific gender-case article transition frequently affect unrelated gender-case settings, with substantial overlap among the most affected neurons across settings. These results argue against a strictly rule-based encoding of German definite articles, indicating that models at least partly rely on memorized associations rather than abstract grammatical rules.

ACL 2026

Understanding or Memorizing? A Case Study of German Definite Articles in Language Models

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

technical paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess similar privileged knowledge about answer correctness, information unavailable through external observation. We train correctness classifiers on question representations from both a model's own hidden states and external models, testing whether self-representations provide a performance advantage. On standard evaluation, we find no advantage: self-probes perform comparably to peer-model probes. We hypothesize this is due to high inter-model agreement of answer correctness. To isolate genuine privileged knowledge, we evaluate on disagreement subsets, where models produce conflicting predictions. Here, we discover domain-specific privileged knowledge: self-representations consistently outperform peer representations in factual knowledge tasks, but show no advantage in math reasoning. We further localize this domain asymmetry across model layers, finding that the factual advantage emerges progressively from early-to-mid layers onward, consistent with model-specific memory retrieval, while math reasoning shows no consistent advantage at any depth.

Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness

LLMs can solve complex tasks by generating long, multi-step reasoning chains. Test-time scaling (TTS) can further improve LLM performance by sampling multiple variants of intermediate reasoning steps, verifying their correctness, and strategically choosing the best steps for continuation. However, existing verification approaches, such as Process Reward Models (PRMs), are computationally expensive, limited to specific domains, and require large-scale human or model-generated annotations. We propose a lightweight alternative for step-level reasoning verification based on probing the internal states of LLMs. We train a transformer-based probe that uses the internal states of the frozen LLM to estimate the credibility of its reasoning steps during generation. Annotation can be generated either by another larger LLM (e.g., DeepSeek-R1) or in a self-supervised manner by the original model itself. The probes are both effective and lightweight, containing fewer than 10M parameters. Across multiple domains, including mathematics, planning, and general knowledge question answering, our probes match or even exceed the performance of PRMs that are up to 810× larger. Our findings suggest that the internal states of LLMs encode their confidence in reasoning processes and can serve as reliable signals for reasoning step verification, offering a promising direction towards scalable and generalizable TTS and introspective LLMs.

Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models

Vision-and-Language Navigation (VLN) poses significant challenges for agents to interpret natural language instructions and navigate complex 3D environments. While recent progress has been driven by large-scale pre-training and data augmentation, current methods still struggle to generalize to unseen scenarios, particularly when complex spatial and temporal reasoning is required. In this work, we propose SkillNav, a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents. Our method decomposes navigation into a set of interpretable atomic skills (e.g., Vertical Movement, Area and Region Identification, Stop and Pause), each handled by a specialized agent. To support targeted skill training without manual data annotation, we construct a synthetic dataset pipeline that generates diverse, linguistically natural, skill-specific instruction-trajectory pairs. We then introduce a novel training-free Vision-Language Model (VLM)-based router, which dynamically selects the most suitable agent at each time step by aligning sub-goals with visual observations and previous actions. SkillNav obtains competitive results on commonly used benchmarks and establishes state-of-the-art generalization to the GSA-R2R, a benchmark with novel instruction styles and unseen environments.

Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents

Existing Multimodal Large Language Models (MLLMs) struggle with 3D spatial reasoning, as they fail to construct structured abstractions of the 3D environment depicted in video inputs. To bridge this gap, drawing inspiration from cognitive theories of allocentric spatial reasoning, we investigate how to enable MLLMs to model and reason over text-based spatial representations of video. Specifically, we introduce Textual Representation of Allocentric Context from Egocentric Video (TRACE), a prompting method that induces MLLMs to generate text-based representations of 3D environments as intermediate reasoning traces for more accurate spatial question answering. TRACE encodes meta-context, camera trajectories, and detailed object entities to support structured spatial reasoning over egocentric videos. Extensive experiments on VSI-Bench and OST-Bench demonstrate that TRACE yields notable and consistent improvements over prior prompting strategies across a diverse range of MLLM backbones, spanning different parameter scales and training schemas. We further present ablation studies to validate our design choices, along with detailed analyses that probe the bottlenecks of 3D spatial reasoning in MLLMs.

Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning

Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) encode it internally. We present the first neuron-level interpretability study of emotion-sensitive neurons (ESNs) in LALMs and provide causal evidence supporting the existence of such units in Qwen2.5-Omni, Kimi-Audio, and Audio Flamingo 3. Across these three widely used open-source models, we compare frequency-, entropy-, mean-deviation-, and contrast-based neuron selectors on multiple emotion recognition benchmarks. Using inference-time interventions, we reveal a consistent emotion-specific signature: deactivating neurons selected for a given emotion disproportionately degrades recognition of that emotion while largely preserving other classes, whereas targeted steering amplifies these units to bias predictions toward the target emotion. These effects arise with modest amounts of identification data and scale systematically with intervention strength. We further observe that ESNs exhibit non-uniform layer-wise clustering with partial cross-dataset transfer. Taken together, our results offer a causal, neuron-level account of emotion decisions in LALMs and highlight targeted neuron interventions as an actionable handle for controllable affective behaviors.

Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models

Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM) reasoning often incur substantial inference costs, due to reliance on long chain-of-thought (CoT) generation, self-consistency sampling methods, or searching under Process Reward Models (PRMs). This paper introduces Guided by Gut (GG), an efficient self-guided TTS framework that enables LLMs to perform step-by-step reasoning at a low cost, without any reward models or verifiers. GG performs a lightweight tree search guided solely by intrinsic confidence signals of the LLM at each reasoning step and improves the reliability of such internal confidence signals by reinforcement learning. Empirical evaluations on challenging mathematical reasoning benchmarks demonstrate that GG enables smaller models (e.g., 1.5B-7B parameters) to achieve accuracy matching or surpassing significantly larger models (e.g., 32B–70B parameters), while reducing GPU memory usage by up to 10×. Compared to TTS with PRMs, GG achieves comparable accuracy with 8× faster inference speeds and 4–5× lower memory usage. Additionally, GG reduces KV cache memory usage by approximately 50% compared to Best-of-N sampling, facilitating more efficient and practical deployment of TTS techniques.

Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence

Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in-the-wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high-quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross-sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various downstream tasks. We will release our code, data, and models to facilitate future research.

WildReward: Learning Reward Models from In-the-Wild Human Interactions

Advancements in Large language models (LLMs) have enabled a variety of downstream applications like story and interview script generation.
However, recent research raised concerns about culture-related fairness issues in LLM-generated content.
In this work, we identify and systematically investigate LLMs' **insider-outsider bias**, a phenomenon where models position themselves as "insiders" of mainstream cultures during generation while externalizing less dominant cultures.
We propose the ***InsideOut*** benchmark with 4,000 generation prompts and three evaluation metrics to quantify this bias through a *culturally situated interview script generation* task, in which an LLM is positioned as a reporter interviewing local people across 10 diverse cultures.
Empirical evaluation on 5 state-of-the-art LLMs reveals that while models adopt insider tones in over 88\% US-contexted scripts on average, they disproportionately default to "outsider" stances for non-Western cultures.
To mitigate these biases, we propose *2 inference-time methods*: a baseline prompt-based **Fairness Intervention Pillars (FIP)** method, and a structured **Mitigation via Fairness Agents (MFA)** framework consisting of a Single-Agent (MFA-SA), a Hierarchical-Agent (MFA-HA), and an autonomous Agentic Planning (MFA-Plan) pipeline.
Empirical results demonstrate that agent-based MFA methods achieve outstanding and robust performance in mitigating the insider-outsider bias:
For instance, on the Cultural Alignment Gap (CAG) metric, *MFA-SA reduces bias in Llama model by 89.70 \% and MFA-HA mitigates bias in Qwen by 82.54\%*.
These findings showcase the effectiveness of agent-based methods as a promising direction for mitigating biases in generative LLMs.

InsideOut: Measuring and Mitigating Insider–Outsider Bias in Interview Script Generation

Large Language Models (LLMs) often produce texts that appear coherent and credible, even when their factual reliability is uncertain. This paper investigates whether such perceived credibility correlates with the pervasive use of generics—generalizations without explicit quantification. We introduce a text-level genericity score derived from clause-level annotations and apply it to argumentative essays produced by humans and LLMs. To analyze how generics are realized in discourse, we employ Rhetorical Structure Theory to examine coherence relations across varying levels of genericity. Results show that according to our genericity metric, human texts are less generic than LLM-produced texts. As regards discourse, higher genericity correlates with less structured, paratactic structures, while for some models coherence is maintained through elaboration relations. Our findings suggest that some LLMs maintain well-structured coherence even in highly generic texts, which might enable them to "camouflage" argumentative texts as informative, enhancing their perceived credibility and persuasiveness.

Discourse Realization of Generics in Human and LLM-generated Texts

Capturing organization-specific domain knowledge remains a critical challenge for deploying cost-efficient language models in specialized tasks like contact center Quality Assurance (QA). While large LMs implicitly capture expert judgment, smaller LMs require explicit evaluation criteria that domain experts struggle to articulate. We introduce Backward Question-based Refinement (\textbf{BQR}), a diagnostic framework that generates backward questions, revealing what a model understood rather than what was asked, to systematically distill implicit reasoning from large LMs into explicit evaluation plans. Through experiments on 12 QA questions, BQR achieves performance improvements on 8 questions with absolute gains of up to 27.8\% in Macro F1. Our analysis establishes empirical parallels to gradient-descent optimization and reveals a cross-family advantage where small LMs benefit more from large LMs of different families. These findings confirm BQR as an effective approach for bridging the gap between implicit expert knowledge and explicit evaluation criteria.

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