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4,637 papers
This work collects data from four different languages, and analyzes how different communicative needs and variations in the listener model influence the informativeness--complexity trade-off, showing that trade-off optimality is not only theoretically achievable but also emerges empirically in learned communication systems.
Overall, internal web-based retrieval functions effectively as a low-latency verification mechanism, but falls short as a reliable IR pipeline, highlighting the need for improved retrieval triggering, query formulation, and evidence-aware confidence calibration in web-enabled LLMs.
A forensic analysis of batch speculative decoding is conducted and it is found that several widely-used implementations silently produce corrupted outputs while reporting competitive speed; failures invisible to metrics like ROUGE.
This work finds that self bias arises from two compounding sources, LLM as a testset and LLM as an evaluator, and their combination amplifies the effect and confirms that the phenomenon extends to open-ended generation on the Chatbot Arena task.
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Transformer Encoder Tree is introduced, a hierarchical, non-autoregressive encoder-only architecture trained with Connectionist Temporal Classification for multilingual translation that eliminates the sequential bottleneck of autoregressive models and supports fully parallel decoding of all tokens across all target languages.
The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains scarce. Here we introduce MAGneT, a novel multi-agent framework for synthetic psychological counseling session generation that decomposes counselor response generation into coordinated sub-tasks handled by specialized LLM agents, each modeling a key psychological technique. Unlike prior single-agent approaches, MAGneT better captures the structure and nuance of real counseling. We further propose a unified evaluation framework that consolidates diverse automatic metrics and expands expert assessment from four to nine counseling aspects, thus addressing inconsistencies in prior evaluation protocols. Empirically, MAGneT substantially outperforms existing methods: experts prefer MAGneT-generated sessions in 77.2% of cases on average across the nine aspects over the strongest baseline, and sessions generated by MAGneT using Llama3-8B-Instruct backbone yield 3.2% higher general counseling skills and 4.3% higher CBT-specific skills on cognitive therapy rating scale (CTRS). An open source Llama3-8B-Instruct model fine-tuned on MAGneT-generated data also outperforms models fine-tuned using baseline synthetic datasets by 6.9% on average on CTRS. We make our code, data and fine-tuned model public.
The introduction of MM-BrowseComp, a novel benchmark comprising 400 challenging, hand-crafted questions designed to evaluate multimodal retrieval and reasoning capabilities, is introduced, establishing MM-BrowseComp as a rigorous new standard for the field.
This work reveals a paradox where a simplified, router-free multi-head model with high inter-head redundancy outperforms complex, diversity-driven baselines and proposes Align-LoRA, a unified and efficient framework that shifts the focus from architectural isolation to representation alignment.
The first fully automated framework that synthesizes high-quality, proof-centric benchmarks from natural language mathematical corpora and a new type of hybrid-formatted questions, named ``$m$-out-of-$n$ multiple judge questions'', specifically designed to enable robust, automatic evaluation while being resilient to guessing and superficial pattern matching inherent in traditional formats are proposed.
It is found that simply training models on CoT data of atomic tasks leads to limited generalization, but minimally modifying CoT formats of constituent atomic tasks to be composable can lead to improvements.
The HeTGB is introduced, a novel benchmark comprising five real-world heterophilic graph datasets from diverse domains, with nodes enriched by extensive textual descriptions that enables systematic evaluation of GNNs, pre-trained language models (PLMs) and co-training methods on the node classification task.
Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constrained instructions (e.g., writing a text from a description or selecting a gendered pronoun). However, little attention has been paid to biases in responses to contextually unconstrained (generic) instructions conveyed by gendered language, particularly masculine generics (MG). MG, found in many gender-marked languages, denote the use of the masculine gender as a supposedly neutral reference to mixed-gender groups or individuals whose gender is unknown or non-binary. Yet, psycholinguistic studies demonstrate that MG are not neutral and systematically induce gender bias. This study investigates how both local and proprietary LLMs are MG-biased when responding to generic prompts in French, examining LLMs'MG bias rates and use of gender-fair language (GFL). We create a 16k+ human noun database from existing lexical resources and evaluate six LLMs on four instruction-response datasets under two conditions: prompts with and without MG. Overall, we find that $\approx$27.57% of LLMs'responses to MG-filtered generic instructions are MG-biased ($\approx$78.55% with MG-containing prompts). Moreover, we find that LLMs rarely use GFL spontaneously. These findings highlight the persistence of MG bias in LLM outputs and models'limited tendency towards GFL strategies.
Machine Intelligence
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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