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The behaviors of Large Language Models (LLMs) as artificial social actors are largely underexplored, particularly in unverifiable scenarios where conventional benchmarking has little to help improve their abilities. Thus, examining their behaviors in such scenarios can help understand and improve LLMs&#39; capabilities of simulating real-world social actors in many tasks such as LLM-empowered social agents. We draw a typical unverifiable scenario--a simplified pull request scenario on \textsc{GitHub} focusing on decision-making based on Activity Overview signal--to investigate how human and LLMs behave. We introduce a systematic method to collect, compare, and reason about human and LLMs&#39; decisions. Our results reveal that there are both similarities and differences between human and LLMs&#39; decisions, and proprietary LLMs generally behave more like human than open-source LLMs do. We further find that human and LLMs may rely on different information and reasoning mechanisms in decision-making. Our study thus urges more future work on human and LLMs decision-making in unverifiable environments.

ACL 2026

Investigating Human and LLMs&#39; Decisions in Unverifiable Environments: A Case Study with GitHub Activity Overview

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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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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.

The behaviors of Large Language Models (LLMs) as artificial social actors are largely underexplored, particularly in unverifiable scenarios where conventional benchmarking has little to help improve their abilities. Thus, examining their behaviors in such scenarios can help understand and improve LLMs' capabilities of simulating real-world social actors in many tasks such as LLM-empowered social agents. We draw a typical unverifiable scenario--a simplified pull request scenario on \textsc{GitHub} focusing on decision-making based on Activity Overview signal--to investigate how human and LLMs behave. We introduce a systematic method to collect, compare, and reason about human and LLMs' decisions. Our results reveal that there are both similarities and differences between human and LLMs' decisions, and proprietary LLMs generally behave more like human than open-source LLMs do. We further find that human and LLMs may rely on different information and reasoning mechanisms in decision-making. Our study thus urges more future work on human and LLMs decision-making in unverifiable environments.

Investigating Human and LLMs' Decisions in Unverifiable Environments: A Case Study with GitHub Activity Overview

technical paper

Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, and the propagation of unwanted patterns during extended dialogue and conversations. Although training-time or data-centric methods attempt to reduce these effects, they are computationally expensive, irreversible once deployed, and slow to adapt to new conversational contexts. Pruning-based methods provide a flexible and transparent way to reduce bias by adjusting the neurons responsible for certain behaviors. However, most existing approaches are static; once a neuron is removed, the model loses the ability to adapt when the conversation or context changes. To address this, we propose a dynamic, reversible, pruning-based framework that detects context-aware neuron activations and applies adaptive masking to modulate their influence during generation. Our inference-time solution provides fine-grained, memory-aware mitigation with knowledge-preserved, more coherent behavior across multilingual single- and multi-turn dialogues, enabling dynamic fairness control in real-world conversational AI.

Fairness Evaluation and Inference Level Mitigation in LLMs

Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model's pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model’s parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based hallucination detection (RHD), where RAG is employed to enhance hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust alignments under evidence perturbations. Experiments across multiple RAG benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.

Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation

Vision-Language Models (VLMs) have shown strong generalization across multimodal tasks, but their capacity to handle sign language translation (SLT), which requires fine-grained spatiotemporal reasoning and linguistic understanding, remains unclear. In this study, we evaluate whether small VLMs (with $\leq$3B parameters) can perform SLT effectively. We perform supervised fine-tuning on four publicly available multilingual SLT datasets, including one German (DGS), two American (ASL), and one Indian (ISL), applying parameter-efficient LoRA to the language decoder while keeping the vision encoder frozen and training only the connector. To evaluate translation quality, we propose entity- and semantics-aware metrics tailored for SLT. We highlight the data imbalance issues present in the above widely used SLT datasets. Our analysis highlights the limitations in applying general-purpose VLMs to SLT, unlike their applicability in other tasks, and provides insights to inform future development of VLMs for SLP, which is essential for building inclusive AI applications.

Can Small Vision–Language Models Perform Sign Language Translation?

Embodied question answering (EQA) in 3D environments often requires collecting context that is distributed across multiple viewpoints and partially occluded. However, most recent vision--language models (VLMs) are constrained to a fixed and finite set of input views, which limits their ability to acquire question-relevant context at inference time and hinders complex spatial reasoning. We propose Chain-of-View (CoV) prompting, a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. CoV first employs a View Selection agent to filter redundant frames and identify question-aligned anchor views. It then performs fine-grained view adjustment by interleaving iterative reasoning with discrete camera actions, obtaining new observations from the underlying 3D scene representation until sufficient context is gathered or a step budget is reached.
We evaluate CoV on OpenEQA across four mainstream VLMs and obtain an average 11.98% improvement in LLM-Match, with a maximum gain of 13.62% on Qwen3-VL-Flash. CoV further exhibits test-time scaling: increasing the minimum action budget yields an additional 2.54% average improvement, peaking at 3.73% on Gemini-2.5-Flash. On ScanQA and SQA3D, CoV delivers strong performance (e.g., 116 CIDEr 31.9 EM@1 on ScanQA and 51.1 EM@1 on SQA3D). Overall, these results suggest that question-aligned view selection coupled with open-view search is an effective, model-agnostic strategy for improving spatial reasoning in 3D EQA without additional training.

CoV: Chain-of-View Prompting for Spatial Reasoning

In complex domains like interior design, user requests are often ambiguous and multimodal. Professional designers address this by asking strategic clarification questions based on hierarchical priorities, a capability lacking in current Vision-Language Models (VLMs). When fine-tuned on dialogue data, existing models often exhibit modality forgetting, overfitting to textual patterns while neglecting visual cues and thus producing hallucinated or visually irrelevant questions. To bridge this gap, we introduce VIDA (Visual Intent-driven Design Assistant), an assistant designed to generate proactive, visually grounded, and strategically prioritized clarification questions. Instead of standard fine-tuning, we propose a strategy-aware alignment framework that evolves from imitation learning to value-driven reinforcement. We utilize Group Sequence Policy Optimization to strictly enforce expert protocols, ensuring the model not only mimics fluent speech but also adheres to optimal inquiry strategies. Crucially, we design a novel hierarchical reward mechanism with Dynamic Intent Binding to align the assistant with professional prioritization standards. To facilitate this research, we construct and release InteriorClarify, a multimodal benchmark dataset comprising 1,016 real-world consultation cases annotated with this three-tier intent hierarchy. Extensive experiments demonstrate that VIDA sets a new state-of-the-art, improving the Strategic Alignment Score (SAS) by 20.59% over SFT baselines and effectively restoring visual grounding capabilities lost during standard fine-tuning.

VIDA: A Visual Intent-driven Design Assistant for Proactive Multimodal Clarification

Semantic segmentation is a core component of discourse analysis, yet existing models are primarily developed and evaluated on high-resource written text, limiting their effectiveness on low-resource conversational varieties. In particular, dialectal Arabic exhibits informal syntax, code-switching, and weakly marked discourse structure that challenge standard semantic segmentation approaches for text. In this paper, we introduce a new multi-genre benchmark (more than 1000 samples) for semantic segmentation in Arabic, focusing on dialectal discourse. The benchmark covers casual telephone conversations, code-switched podcasts, expressive dialogue, and broadcast news, and was annotated and validated by native Arabic annotators. Using this benchmark, we show that segmentation models performing well on MSA news genres degrade on dialectal conversational texts. We further propose a segmentation model that targets local semantic coherence and robustness to discourse discontinuities, consistently outperforming strong baselines on dialectal non-news genres. The benchmark and approach generalize to other low-resource spoken languages.

Linear Semantic Segmentation for Low-Resource Spoken Dialects

Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions. However, existing neuron analyses generally focus on single tasks, limiting the comparability of neuron importance across tasks. Moreover, ranking strategies tend to score neurons in isolation, overlooking how task-dependent information pathways shape the write-in effects of feed-forward network (FFN) neurons. This oversight can exacerbate neuron polysemanticity in multi-task settings, introducing noise into the identification and intervention of task-critical neurons. In this study, we propose HONES (**H**ead-**O**riented **N**euron **E**xplanation & **S**teering), a gradient-free framework for task-aware neuron attribution and steering in multi-task VLMs. HONES ranks FFN neurons by their causal write-in contributions conditioned on task-relevant attention heads, and further modulates salient neurons via lightweight scaling. Experiments on four diverse multimodal tasks and two popular VLMs show that HONES outperforms existing methods in identifying task-critical neurons and improves model performance after steering. Our source code is released at: https://github.com/petergit1/HONES.

From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision–Language Models

Learning unknown knowledge through ICL and RAG can enhance LLM capabilities in specialized fields. While most research focuses on how to identify and utilize such knowledge, little work examines what factors lead LLMs to trust and adopt it, leaving models prone to errors and harmful content. Grounded in extensive pre-experiments, we design five pairs of trust‑enhancing and trust‑diminishing transformations on unknown knowledge to experimentally identify the key trust factors. These findings are further substantiated through a detailed theoretical analysis grounded in the epistemological framework of evidentialism. Based on these insights, we challengingly propose a completely unrestricted and fully randomized jailbreak attack that embeds malicious queries within trust‑enhanced unknown knowledge. In both defended and undefended scenarios, our method achieves 99\% to 100\% ASR on all tested LLMs, including the latest GPT‑5.1, and becomes SOTA. This attack confirms the trust mechanism and exposes a critical and hard-to-defend security risk. Our conclusions provide valuable guidance for understanding trust mechanism of unknown knowledge and for future research.

How Do LLMs "Trust" Unknown Knowledge? An Unknown Knowledge Based Jailbreak Attack

Adapting large language models (LLMs) to domain-specific tasks via fine-tuning is often infeasible: models are protected by intellectual property, while sensitive data cannot be shared due to privacy regulations. A promising paradigm, Offsite Tuning (OT), addresses this challenge by constructing an emulator of the original model. Data owners leverage the emulator to train an adapter on downstream data, which is then plugged back into the original model, enabling knowledge transfer without transmitting either the original model or the raw data. However, emulators constructed by existing OT-based methods often retain substantial inference capabilities, thereby exposing model capability privacy and posing risks of misuse. To address this, we propose Loss Landscape Elevation Offsite Tuning (LLEOT), a framework that secures data privacy as well as model parameter and capability privacy. At its core, Loss Landscape Elevation (LLE) enforces a fixed margin between the loss landscapes of the emulator and the original model. We theoretically demonstrate that LLE simultaneously (i) degrades emulator inference via perplexity amplification and (ii) preserves gradient alignment, ensuring consistent convergence for adapter training. Extensive experiments confirm that LLEOT achieves strong adaptation performance while effectively mitigating emulator misuse. Code is available at https://github.com/Z-eloto/LLEOT.

LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation

While Multimodal Large Language Models (MLLMs) have demonstrated the capacity for multi-modal reasoning, current Referring Expression Comprehension (REC) benchmarks lag behind, predominantly relying on intra-image cues and neglecting the integration of external world knowledge, which significantly impedes the evolution of REC towards real-world applications. This limitation obscures a model's true capability to conduct textual reasoning (entity resolution), resolve spatial location (visual grounding), and verify reference validity (hallucination rejection). To address this, we introduce KnowDR-REC, a targeted audit benchmark comprising 1,042 positive triplets derived from real-world knowledge, along with rigorously matched negative samples. Unlike traditional datasets, we implement a controllable counterfactual evaluation mechanism that subjects textual expressions to single-factor perturbations (entity, relation, or time) to test sensitivity to fine-grained factual changes. Extensive evaluation of 18 state-of-the-art LMMs exposes a critical ``binding hallucination,'' revealing that current high performance is often built on fragile visual shortcuts rather than true understanding. KnowDR-REC thus serves as a pivotal diagnostic instrument, steering future research toward the genuine integration of perception and reasoning.

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