Datasets:
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
reinforcement-learning-from-human-feedback
reinforcement-learning
dialogue
conversational-ai
preference-alignment
License:
metadata
language:
- en
license: apache-2.0
task_categories:
- conversational
- text-generation
tags:
- reinforcement-learning-from-human-feedback
- dialogue
- conversational-ai
- preference-alignment
dataset_info:
- config_name: feedback_decoder_binary
features:
- name: predictions
dtype: int64
- name: labels
dtype: int64
- name: game_turn_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 5115528
num_examples: 7003
download_size: 651037
dataset_size: 5115528
- config_name: feedback_decoder_ternary
features:
- name: predictions
dtype: int64
- name: labels
dtype: int64
- name: game_turn_id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 4936671
num_examples: 7003
download_size: 640213
dataset_size: 4936671
- config_name: human_eval
features:
- name: correctness
dtype: string
- name: feedback
dtype: string
- name: delta_clicks
sequence: string
- name: game_id
dtype: string
- name: turn_id
dtype: int64
- name: round
dtype: int64
- name: policy_name
dtype: string
- name: targets
sequence: string
- name: selected
sequence: string
- name: deselected
sequence: string
- name: context
sequence: string
- name: end
dtype: int8
- name: currently_selected
sequence: string
splits:
- name: train
num_bytes: 3190406
num_examples: 8111
download_size: 347537
dataset_size: 3190406
- config_name: interaction
features:
- name: game_id
dtype: string
- name: turn
dtype: int64
- name: end
dtype: string
- name: s comments
dtype: string
- name: speaker
dtype: string
- name: len
dtype: string
- name: clicks
sequence:
sequence: string
- name: context
sequence: string
- name: targets
sequence: string
- name: chat
sequence: string
- name: dataset_alias
dtype: string
- name: policy_name
dtype: string
- name: date
dtype: date32
- name: round
dtype: int64
splits:
- name: train
num_bytes: 8775426
num_examples: 7920
download_size: 2120499
dataset_size: 8775426
- config_name: turn
features:
- name: chats
sequence: string
- name: clicks
sequence:
sequence: string
- name: targets
sequence: string
- name: game_id
dtype: string
- name: end
dtype: int8
- name: context
sequence: string
- name: turn_id
dtype: int8
- name: currently_selected
sequence: string
- name: deselected
sequence: string
- name: selected
sequence: string
- name: chat_feedback
dtype: string
- name: game_turn_id
dtype: string
- name: prob_action
dtype: float64
- name: dataset_alias
dtype: string
- name: policy_name
dtype: string
- name: date
dtype: date32
- name: round
dtype: int64
splits:
- name: train
num_bytes: 45674609
num_examples: 59431
download_size: 6438021
dataset_size: 45674609
configs:
- config_name: feedback_decoder_binary
data_files:
- split: train
path: feedback_decoder_binary/train-*
- config_name: feedback_decoder_ternary
data_files:
- split: train
path: feedback_decoder_ternary/train-*
- config_name: human_eval
data_files:
- split: train
path: human_eval/train-*
- config_name: interaction
data_files:
- split: train
path: interaction/train-*
- config_name: turn
data_files:
- split: train
path: turn/train-*
Retrospective Learning from Interactions (Respect) Dataset
This dataset supports Retrospective Learning from Interactions (Respect), a paradigm introduced in the paper The Era of Real-World Human Interaction: RL from User Conversations.
The paper introduces Reinforcement Learning from Human Interaction (RLHI), a novel approach that learns directly from in-the-wild user conversations. This enables continual model improvement and multifaceted alignment of conversational models, moving beyond traditional pre-annotated, expert-generated human feedback. The dataset facilitates two complementary methods: RLHI with User-Guided Rewrites and RLHI with User-Based Rewards, linking long-term user personas to turn-level preferences.
- Paper: The Era of Real-World Human Interaction: RL from User Conversations
- Project Page: https://lil-lab.github.io/respect
- GitHub Repository: https://github.com/lil-lab/respect
Sample Usage
You can load the data and associated checkpoints as follows:
from datasets import load_dataset
from transformers import Idefics2ForConditionalGeneration
from peft import PeftModel
import torch # Ensure torch is imported
# Download data
ds = load_dataset("lil-lab/respect", name="turn", split="train")
# Download checkpoints
checkpoint = "HuggingFaceM4/idefics2-8b"
model_id = 'lil-lab/respect'
model = Idefics2ForConditionalGeneration.from_pretrained(
checkpoint, torch_dtype=torch.bfloat16)
peft_model = PeftModel.from_pretrained(
model, model_id, adapter_name="r6_bp", revision="r6_bp")