helpfulness
chat-v2-anthropic-helpfulnessreview_helpfulness_prediction
Dataset Card for Review Helpfulness Prediction (RHP) Dataset
Dataset Summary
The success of e-commerce services is largely dependent on helpful reviews that aid customers in making informed purchasing decisions. However, some reviews may be spammy or biased, making it challenging to identify which ones are helpful. Current methods for identifying helpful reviews only focus on the review text, ignoring the importance of who posted the review and when it was posted.… See the full description on the dataset page: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction.ultrafeedback-gpt-3.5-turbo-helpfulness
UltraFeedback GPT-3.5-Turbo Helpfulness Dataset
Summary
The UltraFeedback GPT-3.5-Turbo Helpfulness dataset contains processed user-assistant interactions filtered for helpfulness, derived from the openbmb/UltraFeedback dataset. It is designed for fine-tuning and evaluating models in alignment tasks.
Data Structure
Format: Conversational
Type: Unpaired preference
Column:
"pompt": The input question or instruction provided to the model.
"completion": The… See the full description on the dataset page: https://huggingface.co/datasets/trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness.Meta-Llama-3-8B-Instruct_ultrafeedback-annotate-judge-mtbench_cot_helpsteer_helpfulnesshelpfulness-safety-calibration-dpo-100k
Helpfulness-Safety Calibration DPO (100K)
100,000 DPO preference pairs for calibrating the helpfulness-safety tradeoff in language models. Each example contains a prompt, a chosen response (correct handling), and a rejected response (incorrect handling) — covering both over-refusal and under-refusal failure modes.
Motivation
Safety-trained models often swing between two failure modes:
Over-refusal: Refusing legitimate requests because they superficially resemble… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/helpfulness-safety-calibration-dpo-100k.ALIA-2606-DPO-helpfulness
Dataset Card for BSC Multilingual Synthetic Helpfulness Preferences
Dataset Summary
This dataset consists of synthetic helpfulness preference data generated to align language models across five languages: Catalan, Spanish, English, Basque, and Galician.
Building on the PKU-SafeRLHF and Tulu 3/Ultrafeedback methodologies for creating preference data, this dataset leverages an LLM-as-a-judge approach to automatically score and pair model responses to a massive pool… See the full description on the dataset page: https://huggingface.co/datasets/BSC-LT/ALIA-2606-DPO-helpfulness.