A key question in current AI alignment research is how to make AI algorithms learn moral values. Because human morality is highly context-dependent, actions are judged not only by their outcomes but by the context in which they occur. We present COMETH (Contextual Organization of Moral Evaluation from Textual Human inp...
Geoffroy Morlat, Marceau Nahon, Augustin Chartouny et al.· 0 citations
In our daily lives, newspapers are an essential information source that impacts how the public talks about present-day issues. However, effectively navigating the vast amount of news content from different newspapers and online news portals can be challenging. Newspaper headlines with sentiment analysis tell us what th...
Mirza Raquib, Munazer Montasir Akash, Tawhid Ahmed et al.· 0 citations
Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG could also lead to performance degradation due to imperfect retrieval and the model's limited ability to leverage retrieved content. In this work, we evaluate the robustness of LLMs...
Shuyang Cao, Karthik Radhakrishnan, David Rosenberg et al.· 0 citations
Role-playing AI personas today do not grow: they hold a fixed character, so the relationship a user builds with them has nothing to accumulate on. We introduce AutoPersonas, a multi-timescale engine that applies recursive self-improvement (RSI) to persona growth: rather than improving its intelligence, the persona recu...
Mengchen Li· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
LLM agents increasingly decide from evidence assembled by upstream systems: retrievers choose documents, recommenders choose posts, and memory systems choose prior events. Existing evaluations usually hold this evidence fixed, missing failures in which individually ordinary items form a systematically one-sided context...
We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interest...
Iordanis Fostiropoulos, Muhammad Rafay Azhar, Abdalaziz Sawwan et al.· 0 citations
A language model can give the wrong answer even when the correct answer is decodable from its intermediate states. To study this gap between decodability and selection, we distinguish \textit{read} from \textit{write} at the first answer token. Read asks whether the gold token can be decoded from intermediate residual...
LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a fixed number of iterations. We argue that practical optimization should maximize gain per...
Large language models are increasingly used where small syntactic errors matter, yet character-level reasoning is still evaluated mostly through isolated probes and aggregate accuracy. We introduce SyntaxBench, a diagnostic benchmark and statistical evaluation framework for character-level reasoning. It contains five c...
Accident, defect and outage investigations end with a decision that ordinary question answering never faces: whether the evidence gathered so far is enough to close the case. We study this decision for LLM investigators, which request evidence from a case file, revise their hypotheses, and either close the case with a...
Adapting large language models to an individual author's style from a few examples is challenging, and scientific writing sharpens the difficulty: formal conventions leave little surface variation, and authors write about their own topics, so extracted ``style''easily entangles with content. We study style-conditioned...
L. Popp, Danni Liu, Supriti Sinhamahapatra et al.· 0 citations
Open-weight language models can be downloaded, modified, and deployed beyond their developers'control, limiting the effectiveness of centrally enforced safeguards. Recent work has therefore proposed \emph{trigger-tag} mechanisms that produce a detectable signal when a model is used under a target condition, such as gen...
Toluwani Aremu, Manit Baser, M. Gurusamy et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
