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human-feedback

topic3 events
papersTODAY 04:00 UTC

Paper Analyzes Selection Bias When Model Edits Target Localized Spans

A new arXiv paper examines what happens when human corrections are applied only to identified editable spans of a model's output. The authors decompose the localized gradient into edited and untouched portions at a fixed checkpoint, showing that selective feedback channels can amplify relative selection bias. They also study gradient geometry, target mismatch, and importance weighting as factors in this effect.

research/fine-tuning/bias/training-data
papersTODAY 04:00 UTC

arXiv Paper Reexamines Human Feedback for Robot Preference Learning

A new arXiv paper examines how robots typically build reward models of human preferences, a process that starts with collecting limited direct feedback such as positive or negative signals. The authors argue that the assumptions behind this standard three-step pipeline deserve reconsideration in the context of human-robot collaboration. The work is cross-listed in the cs.AI category.

human-robot-interaction/preference-learning/reinforcement-learning/research
papersTODAY 04:00 UTC

Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions

Researchers propose using recordings of how listeners' eyes move while they interpret a speaker's description as a training signal for vision-language models. By converting these gaze scanpaths into incremental feedback, the models learn to produce referring expressions that are more pragmatically suited to the listener. The work is presented as an arXiv preprint in the computation and language category.

vision-language/gaze/fine-tuning/referring-expressions