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Computation & Language / NLP
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Newest papers, page 68
Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research
Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep.Environment Duels: A Microcosm of LLM Co-Evolution
Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create.Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction.LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and…Protection Is Not One Event: A Survey of RAG Protection Research by Measured Endpoint
Yet RAG protection studies use incompatible evaluation metrics, datasets, and threat models.Measuring Research Progress and Linguistic Inclusion in Low-Resource Languages
Large Language Models (LLMs) have accelerated research on low-resource and multilingual NLP, but their effectiveness in creating an equitable landscape remains unmapped.AQL-Lite: Enabling Efficient Knowledge Acquisition for Arabic LLMs via Data-Centric Continual Pretraining
Training Arabic large language models faces challenges from noisy web data, dialectal variation, and the high cost of continual pretraining (CPT).Consistency Is Not Validity: Self-Rewarded Moral Argumentation Makes Language Models Decisive but Pliable
LLMs are increasingly consulted for moral judgment.CiteAnchor: Source-Aware Grounding for Hallucinated Citation Detection and Resolution
Hallucinated citations make it harder to trace sources and verify the evidence behind scientific claims.Probing and Steering for Safety-Relevant Behaviours in Large Language Models
Activation probes are computed directions in a language model's residual stream, used both to detect safety-relevant behaviours and, by adding their direction back into the stream, to steer them.Climbing the Ladder: Automating Data Pipelines and Reproducing Baselines in Low-Resource Settings
The massively multilingual translation systems that now include Sicilian learned it from noisy, non-standardised web text.MemPlan: Memory-Conditioned PDDL Planning for Partially Observable Text Environments
LLM agents are flexible in language interaction, but using them as long-horizon planners is costly and prone to hallucinated or invalid actions.Text-Only LLM Correctors Break Correct Transcripts: A Repair-Damage Analysis of Audio Grounding in ASR Error Correction
LLM correctors of ASR output are judged by aggregate WER, which hides what they fix and what they break.SIM-UT: A User Simulation based Evaluation Framework for Medical Triage Chatbots
LLM-based chat agents are increasingly used for complex, multi-turn, multilingual medical triage in low- and middle-income countries (LMICs), yet evaluation methods focus on single-turn…X-pressing Intimacy: Mental Health Disclosure After Twitter Acquisition
Social media platforms provide spaces for individuals to disclose mental health experiences and seek support, yet how these disclosures evolve during major shifts in platform governance remains…Agent Mosaic: Recognizing Confidentiality Leaks in Agentic LLM Systems
Modern LLM agents can retrieve information from accessible documents, and then disclose a sensitive conclusion assembled from them.Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models
Recent advances in search agents have enabled large language models to tackle complex tasks by retrieving and synthesizing external information.Rethinking SAE Evaluation: An Atomic Interpretable Unit Framework Reveals Hidden Polysemanticity and Redundancy
Sparse autoencoders (SAEs) were originally motivated by discovering monosemantic features, a property that characterizes individual features but does not specify what the learned feature set should…Why Bother Me Again? Benchmarking Personal Agent Memory for Interaction Preferences
Personal intelligence systems are taking on increasingly complex everyday tasks, yet repeated questions, unnecessary confirmations, and unwanted outreach can turn capable assistance into interaction…Rethinking the Evaluation of LLM Fact-Checkers
Large language models (LLMs) are widely used to answer factual questions, but their answers can contain errors and must be fact-checked claim by claim.