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Computation & Language / NLP
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Newest papers, page 192
From Tweets to Trades: Analyzing the Influence of Public Mood over Stock Market Performance in Turkiye
This study examines whether domain-specific public mood is associated with stock-market dynamics and whether these relationships vary across communication domains and market conditions.AutoDataBench: A Data-centric Testbed for Accelerating Auto Research
Existing auto-research benchmarks often entangle multiple sources of improvement, including training frameworks, hyperparameters, compute budgets, and data, making it difficult to attribute why one…JuryFlow: Disagreement-Guided Human-in-the-Loop Multi-Agent Evaluation
Large language models (LLMs) are increasingly deployed as automated judges for AI-generated content, yet a single judge is unreliable and even a panel of judges leaves a hard residue: when judges…OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction
Opioid overdose remains a major clinical and public health burden, highlighting the need for scalable approaches to identify patients at high risk.Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses.Persistent Context Graphs for Efficient Memory Compaction in LLM Agents
As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons.On the (In)effectiveness of AMR Augmentation for Large Language Models
While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit---or lack thereof---of AMR augmentation for modern LLMs is thus far unclear.Debias It Yourself: Teaching LLMs Cognitive Bias Mitigation Interventions
Bias has long been studied in social psychology and cognitive science, where decades of research have produced a body of validated interventions that reduce stereotypical thinking and prejudiced…Learning Functional Subspaces for Neural Network Compression
Modern transformers pair impressive capabilities with substantial memory and compute demands.Index-Translate: A Multilingual Translation Model Family -- Text, Speech, Controlled Dubbing, and Long-Document Translation
We introduce Index-Translate, a multilingual translation model family that combines a shared multilingual foundation with specialized training for general translation, instruction following, speech…Provably Tractable NFA-Constrained Language Generation via HMMs
Constrained generation aims to sample from language models (LMs) conditioned on hard constraints.Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves
Sampling several answers and keeping the one a verifier scores highest is one of the simplest ways to buy accuracy at test time.MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories
Long-term egocentric video enables personalized AI assistants to reason about daily life.SCB: SpeechConversationBench for Evaluating Multi-Turn Reasoning in Speech-to-Speech Models
Speech-to-speech systems must solve tasks whose requirements emerge across conversational turns.PhantomEnvironments: Training LLM Agents in Fictional Worlds
Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply.Distribution Matching Distillation for Continuous Diffusion Language Models
Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (NFEs).Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Converting free-text radiology reports into structured labels supports cohort building, quality assurance, and monitoring of clinical imaging models, but the strongest label extractors are hosted…Decision-Oriented Recommendation Reranking: An Empirical Study of Jev
Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation quality and serving efficiency.cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents
Computer use agents (CUAs), which use graphical user interfaces (GUIs) to complete tasks on a computer, have recently surpassed human performance on many standard benchmarks, including difficult…Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning
Unlearning a fact in one language does not guarantee its removal in others as changing the query or even the requested answer language can reopen seemingly forgotten knowledge -- a cross-lingual…