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Computation & Language / NLP
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Newest papers, page 189
ViLegalExpert: A Large-Scale Benchmark for Vietnamese Legal Retrieval and Question Answering from Real-World Consultations
Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources.Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures
Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations.RAIM: Robust Aggregation of Inexpensive Models for Hallucination Detection
Automatic evaluation of faithfulness increasingly relies on a large language model acting as a judge, yet the most reliable judges are proprietary frontier models, costly and ill-suited to…4MT-VLM: How Coarse Is a VLMs Cognitive Map?
An agent that moves must recognise a place from a viewpoint it has never seen.Concept Subspaces Compute Beyond the Logit Lens: A Weights-Only Test for Locating Representations Upstream of Readout
A concept subspace's effect on model behavior does not establish how it relates to the output readout.A Tilted Bowl Is Not a Slippery Slope: Compressing Looped Models
Looped models reason by applying the same block of weights many times, so compressing that block saves memory traffic on every loop.NarrativeSteward: Coordinating Delegation, Guidance, and Verification in Agent-Assisted Interactive Narrative Authoring
Autonomous AI agents can turn authors' goals into interactive narratives by independently organizing and carrying out generation and revision.Taming Speculative Search for Test-Time Scaling in LLM Serving
Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, substantially enhancing accuracy on challenging tasks…Understanding as No-Arbitrage: Bounded Dutch Books as a Definition and Training Objective for Language Models
Does a language model merely predict tokens, or does it understand what it says?Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models
Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SLMs) are easier to deploy locally yet remain…Working Around the Compute Ceiling: Byte-Exact Memory in Galahad Makes LLM Reading a One-Time Cost LLM Reading a One-Time Cost
A transformer language model performs a bounded amount of computation per token, and recent work by Vishal Sikka, former CEO of Infosys, argues that this bound limits which tasks a model can carry…Ready2Blend: From Natural-Language Instructions to Composable Alignment Prompts
Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors.Making Grid Beam Search Less Greedy
A common formalism for constraining the output of autoregressive text generation models involves lexical constraints, words or phrases which are required to occur in the generated text.Exploring Heterogeneous Model Merging Approach for Complex Knowledge Transfer
Specialized models encode task-oriented behavior, but transferring that behavior to a general language model usually requires training, distillation, or representation alignment.TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic
Argument Mining (AM) is a critical NLP task that remains significantly under-resourced in Arabic.Can Computation from Earlier Problems Help LLMs Solve New Ones?
Large language models often solve independent problems in the same conversation.QuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code
Large language models are strong general-purpose code generators, but executable algorithmic trading remains a demanding specialization target: a model must translate a natural-language strategy…DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements
Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions.Synthetic Data Characterization via Training Dynamics
Interpreting properties of LLM-generated data is important for understanding its utility and limitations across learning tasks.From Speech to Editable Concepts: Probing Emotion Recognition with Concept Bottleneck Models
Speech emotion recognition (SER) is the task of assigning emotion labels to utterances.