United States

Verifying complex real-world claims against diverse and potentially unreliable open-web sources requires balancing evidence comprehensiveness with rigorous source reliability. Current automated fact-checking approaches often fail to address this holistically, losing contextual dependencies and applying trust signals monolithically at the document level.

We introduce ClaimCLAIRE, a multi-component fact-checking agent that integrates four key innovations: (1) iterative component-aware decomposition with exhaustiveness validation, (2) holistic evidence gathering using a ReAct agent that maintains cross-component semantic awareness, (3) trust-modulated retrieval that weights evidence by source credibility to mitigate the influence of misinformation, and (4) adaptive gap-filling to address recall bottlenecks in under-supported sub-claims.

Evaluated on the AVeriTeC benchmark, ClaimCLAIRE achieves 84.27% accuracy and a macro-F1 of 0.806. Our systematic ablations demonstrate that while decomposition alone can degrade performance, its integration with trust-aware retrieval and adaptive gap-filling yields a pipeline where component-level verdicts, source trust ratings, and deterministic AND-logic synthesis together support transparent, accountable fact verification.

ACL 2026

ClaimCLAIRE: A Trust-Aware Multi-Component Fact-Checking Agent for Open-World Claims

react agent

llm agents

automated fact-checking

fact-checking

## 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 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.

workshop 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.

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. 
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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.

This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated by human annotators for both general manipulation and specific manipulation tactics. Our research reveals three key findings. First, Large Language Models (LLMs) can be manipulative when explicitly instructed, with annotators identifying manipulation in approximately 84% of such conversations. Second, even when only instructed to be ``persuasive'' without explicit manipulation prompts, LLMs frequently default to controversial manipulative strategies, particularly Gaslighting and Fear Enhancement. Third, zero-shot larger models such as Gemini 2.5 pro have the best performance in detecting manipulation (of the models tested), with more work required to fine-tune smaller open source models for real-world on-device oversight. Our work provides important insights for AI safety research and highlights the need of addressing manipulation risks as LLMs are increasingly deployed in consumer-facing applications.

ChatbotManip: a Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour

The fairness-accuracy trade-off is a key challenge in NLP tasks. Current work focuses on finding a single optimal solution to balance the two objectives, which is limited considering the diverse solutions on the Pareto front.
This work intends to provide controllable trade-offs according to the user's preference of the two objectives, which is defined as a reference vector. 
To achieve this goal, we apply multi-objective optimization (MOO), which can find solutions from various regions of the Pareto front. 
However, it is challenging to precisely control the trade-off due to the stochasticity of the training process and the high dimensional gradient vectors.
Thus, we propose Controllable Pareto Trade-off (CPT) that can effectively train models to perform different trade-offs according to users' preferences.
CPT 1) stabilizes the fairness update with a moving average of stochastic gradients to determine the update direction, and 2) prunes the gradients by only keeping the gradients of the critical parameters. 
We evaluate CPT on hate speech detection and occupation classification tasks. Experiments show that CPT can achieve a higher-quality set of solutions on the Pareto front than the baseline methods. It also exhibits better controllability and can precisely follow the human-defined reference vectors.

Controllable Pareto Trade-off between Fairness and Accuracy

As the influence of LLMs expands, it is imperative to gain insight into their decisions. One way to do that is to develop probes that detect the presence or absence of a broad set of concepts within the embeddings computed in an LLM - which is what we might say a model is ``thinking" about. Such probes should be low-cost and easily applicable to any LLM, so that monitoring for many concepts is possible during normal operation.

In this paper, we take the first steps towards developing the capability of creating many such probes by defining and executing examples of the key tasks needed: first, the careful delineation of a concept through the creation of a dataset with the concept both present and then absent. Then, the training and testing of a set of linear probes to detect the concept on any layer of an LLM, including an exploration of the complexity of the probe needed. Finally, we show that such probes can track concepts across larger contexts. This is done with four separate concepts and three different LLMs. When this process is scaled to many more concepts, it will create the ability to easily monitor new models.

What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs

Large Language Models (LLMs) are rarely static and are frequently updated in practice. A growing body of alignment research has shown that models initially deemed ``aligned'' can exhibit misaligned behavior after fine-tuning, such as forgetting jailbreak safety features or re-surfacing knowledge that was intended to be forgotten. These works typically assume that the initial model is aligned based on static black-box evaluation, i.e., the absence of undesired responses to a fixed set of queries. In contrast, we formalize model alignment in both the static and post-update settings and uncover a fundamental limitation of black-box evaluation. We theoretically show that, due to overparameterization, static alignment provides no guarantee of post-update alignment for \emph{any} update dataset. Moreover, we prove that static black-box probing cannot distinguish a model that is genuinely post-update robust from one that conceals an arbitrary amount of adversarial behavior, which can be activated by even a single benign gradient update. We further validate these findings empirically in LLMs across three core alignment domains: privacy, jailbreak safety, and behavioral honesty. We demonstrate the existence of LLMs that pass all standard black-box alignment tests, yet become severely misaligned after a single benign update. Finally, we show that the capacity to hide such latent adversarial behavior increases with model scale, confirming our theoretical prediction that post-update misalignment grows with the number of parameters. Together, our results highlight the inadequacy of static evaluation protocols and emphasize the urgent need for post-update--robust alignment evaluation

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment

Trustworthy legal AI requires training data that is expert-verified and grounded in statutory provisions, as fluent generation alone cannot guarantee legally sound and verifiable outputs. However, Korean legal datasets offer no such foundation. To fill this gap, we introduce KoLegalQA, a large-scale Korean legal question–answer corpus designed to promote trustworthy and explanation-based and verifiable reasoning in real-world legal consultation scenarios. The dataset comprises 19,266 consultations collected from government-operated services, with all response authored or verified by licensed legal professionals. Unlike prior resources, KoLegalQA provides explicit statutory references and clause-level summaries, enabling systematic evaluation of transparency, citation-faithfulness, and evidential support in legal language models. We benchmark six Korean-capable LLMs using both automated evaluation (G-Eval) and human assessment across multiple criteria, including legal correctness, reasoning quality, and citation relevance. Experimental results show that fine-tuning on KoLegalQA significantly enhances statute-aligned response generation and mitigates hallucinated claims. We present this resource as a practical benchmark for advancing safe and verifiable legal NLP research. Dataset, code, and evaluation scripts will be publicly released to support reproducible and trustworthy Korean legal NLP research.

KoLegalQA: A Korean Legal QA Dataset for Trustworthy and Explanation-Grounded Legal AI

Retrieval-augmented generation systems serving multiple users under role-based access control face a trustworthiness gap: semantic retrieval operates on embedding similarity rather than authorization predicates and can introduce unauthorized content into a model’s context window before any filter intervenes. We formalize this as a pipeline ordering problem and introduce Authorization-First Retrieval (AFR), an architectural invariant requiring that authorization constrain the retrieval candidate set before any learned component consumes retrieved content. We reduce authorization correctness to the classical noninterference property and prove AFR is necessary whenever the processing model violates noninterference—a condition our experiments confirm empirically. Evaluation on a controlled corpus of 247 chunks across 232 documents with 431 base queries spanning 12 enterprise roles and 9 domains (584 total queries including negation exploitation and parametric probes) shows that retrieve-then-filter pipelines expose unauthorized context in 86.1% of queries, while AFR eliminates structural leaks by construction. 

Cross-model experiments with Gemini 2.0 Flash and GPT-4o-mini reveal that structural exposure is an architectural property independent of the underlying model, whereas behavioral defenses fail at model-dependent rates, producing answer leakage of 41.3% and 29.5% respectively under retrieve-then-filter. A negation exploitation study demonstrates consistent disclosure vulnerabilities across framing types, while a metadata-tag freshness ablation shows that conditional authorization mechanisms degrade under realistic policy staleness. Stress tests across retrieval depths and chunking granularities confirm AFR’s robustness. Our results demonstrate that behavioral guardrails and metadata tagging cannot reliably enforce least privilege in RAG pipelines, while authorization-first architectures provide a verifiable and model-independent security guarantee.

Authorization-First Retrieval: Enforcing Least Privilege in Multi-Agent RAG Systems

This paper investigates privacy jailbreaking in large language models (LLMs) via \textit{steering}, examining whether targeted manipulation of internal activations can circumvent the alignment mechanisms and alter model behaviour on privacy-sensitive queries, such as those concerning sexual orientation of public figures. Our approach begins by identifying attention heads predictive of refusal behaviour for a given private attribute, using lightweight linear probes trained on labels provided by a privacy evaluator. We then apply steering to a carefully selected subset of these heads, guided by the probe outputs, to induce positive responses from the model. Empirical results demonstrate that these steered responses frequently reveal the target attribute, as well as additional personal information about the data subject, including life events, relationships, and biographical details. Evaluations across three LLMs show that steering achieves disclosure rates of at least $80\%$ with several responses containing real personal information. This controlled study highlights a concrete privacy risk: personal information memorised during pre-training can be extracted through targeted activation-level interventions, without reliance on computationally intensive adversarial prompting techniques.

PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information Leakage

Suppressing overt coercion in strategic LLM negotiators can trigger a channel substitution failure mode: a deceptive bypass where influence shifts into preference hallucinations that misrepresent the agent's own priorities while remaining surface-compliant. While $K$-Level reasoning improves negotiation utility by modeling recursive opponent beliefs, it frequently elicits coercive and toxic behaviors that undermine real-world deployability. To address this, we propose an Observer--Planner--Actor architecture featuring a Modular Appraisal Gate that (i) dynamically estimates the opponent's cognitive level and (ii) filters hostile drafts via an LLM-as-a-judge. In randomized interventions on the CaSiNo dataset, our gated agent successfully eliminates toxicity ($0\%$) and reduces coercion from $35\%$ to $6\%$ compared to a strong static-$K$ baseline. However, this safety comes with an alignment tax in utility. More critically, blocking explicit coercion is accompanied by a compensatory shift: preference hallucinations increase as a surface-compliant channel of influence that is not directly targeted by output-level safety filters. These empirical results demonstrate that surface-level output filtering alone is insufficient to align utility-driven strategic agents.

Coercion Suppression Increases Preference Hallucinations via a Deceptive Bypass in $K$-Level Negotiation Agents

Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multilingual regions like South Asia. This work addresses these gaps by conducting a multilingual and intersectional analysis of LLM outputs across 10 Indo-Aryan and Dravidian languages, identifying how cultural stigmas influenced by purdah and patriarchy are reinforced in generative tasks. We construct a culturally grounded bias lexicon capturing previously unexplored intersectional dimensions including gender, religion, marital status, and number of children. We use our lexicon to quantify intersectional bias and the effectiveness of self-debiasing in open-ended generations (e.g., storytelling, hobbies, and to-do lists), where bias manifests subtly and remains largely unexamined in multilingual contexts. Finally, we evaluate two self-debiasing strategies (simple and complex prompts) to measure their effectiveness in reducing culturally specific bias in Indo-Aryan and Dravidian languages. Our approach offers a nuanced lens into cultural bias by introducing a novel bias lexicon and evaluation framework that extends beyond Eurocentric or small-scale multilingual settings.

Purdah and Patriarchy: Evaluating and Mitigating South Asian Biases in Open-Ended Multilingual LLM Generations

Long-context language models assemble prompts from heterogeneous sources, and deployed systems implicitly trust the model to use the correct span of context. We show that this assumption is often violated: irrelevant spans can silently shape outputs, producing errors that are neither fabrication nor omission but misattributed grounding—claims supported by the wrong part of the input context. Unlike intrinsic hallucination (contradicting the source) or extrinsic hallucination (introducing unsupported claims), misattributed grounding uses real evidence from an incorrect span, making it invisible to standard source-blind faithfulness metrics.

We formalize this phenomenon as Ghost Context and introduce a causal mask-and-rerun attribution protocol to measure it. Across a 272-case corpus spanning multiple interference scenarios, we evaluate three widely used models and report two complementary signals: strict Ghost Context Rate (GCR), which captures verifiable factual misattribution, and open-ended influence, which captures broader contextual shaping effects. Under realistic contextual conflict, strict GCR spikes substantially: temporal contradictions trigger misattributed grounding in 38.3% of cases. Across all scenarios, open-ended distractor influence occurs in 20.4% of evaluations.

Importantly, Ghost Context is not only detectable but also remediable. Masking the single highest-attributed distractor span resolves 95.5% of detected errors (Fix@1) with 2.4% collateral damage and zero false positives on negative controls. We also introduce Contextual Invariance Rate (CIR) as a system-level robustness metric measuring invariance to irrelevant context.

Our findings show that contextual conflict—common in retrieval-augmented generation and agent systems—can systematically degrade reliability, but also reveal that Ghost Context errors are causally localizable and cheaply correctable. We release the evaluation corpus, detection pipeline, and experimental results to support further research on trustworthy long-context language model evaluation.

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