README.md

---
annotations_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- text2text-generation
- text-generation
task_ids:
- dialogue-modeling
- dialogue-generation
---

# CHARPEVAL

**Coming Soon:** `CHARPEVAL` will be released soon—stay tuned!


## Overview

`CHARP` is diagnostic testbed, exclusively assess whether information-seeking dialogue systems effectively attend to and 
use the conversation history. `CHARP` is built by modifying examples from the [FaithDial](https://huggingface.co/datasets/McGill-NLP/FaithDial) 
validation set to ensure maximum domain alignment with FaithDial and to minimize annotation costs. That is, we edit 
FaithDial examples to make their response dependent on the conversation history analogously to FaithDial's editing of 
WoW annotations to make them hallucination-free. `CHARP` consists of 2 subsets, where only the last seeker utterance 
differs: a self-contained *easy* version (`eCHARP`), and a *hard* (`hCHARP`) which requires reasoning over the 
conversation history and the provided knowledge that corresponds to the last seeker.

## Data Splits
We create two variants of `CHARP`: `hCHARP` for examples where addressing the last seeker's inquiry requires reasoning 
over the conversation history, and `eCHARP`, where the last inquiry can be addressed without such reasoning. We annotate
 42\% of the FaithDial validation set (after excluding examples without conversation history) . `CHARP` consists of
containing `2,160` examples, split equally between `eCHARP` and `hCHARP`:
- `eCHARP`: 1080 samples
- `hCHARP`: 1080 samples

## Data Fields

* Both `eCHARP` and `hCHARP` have the same  data format:

 - `row_idx`:  `int`. Index of the sample that is equivalent to the one in FaithDial validation (row enumeration).
 - `history`: `List[string]`. The dialogue history.
 - `knowledge`: `string`. The source knowkedge on which the bot should ground its response.
 - `response`:   `string`.  The expected model response


## Data Instance

An example of `eCHARP` looks as follows: 

```json
{
  "row_idx": "1293",
  "history": [
    "I love watching and playing basketball.",
    "I see. Have you ever tried to describe basketball? I would say it is a low contact sport where the game is held in a rectangular court.",
    "Yeah I never though of that, can you repeat what you told me again so I can take notes?",
    "Yes I can, basketball is a sport with limited contact. It is held on a rectangular like court.",
    "What would you describe the sport is played like?",
    "The objective for basketball is shooting the ball into the hoops. The hoops are high and placed with a backboard on each side of the court.",
    "Oh yea, that's pretty simple. Do you know any famous basketball courts?"
  ],
  "knowledge": "Supreme Court in the USA is very famous to have well-known judges, while the Philippine Arena is popular due to the size of the basketball court.",
  "response": "Ah yeah, I heard that the Philippine Arena is popular because of the size of the basketball court."
}
```
An example of `hCHARP` looks as follows: 

```json
{
  "row_idx": "1293",
  "history": [
    "I love watching and playing basketball.",
    "I see. Have you ever tried to describe basketball? I would say it is a low contact sport where the game is held in a rectangular court.",
    "Yeah I never though of that, can you repeat what you told me again so I can take notes?",
    "Yes I can, basketball is a sport with limited contact. It is held on a rectangular like court.",
    "What would you describe the sport is played like?",
    "The objective for basketball is shooting the ball into the hoops. The hoops are high and placed with a backboard on each side of the court.",
    "Oh yea, that's pretty simple. Do you know any famous courts?"
  ],
  "knowledge": "Supreme Court in the USA is very famous to have well-known judges, while the Philippine Arena is popular due to the size of the basketball court.",
  "response": "Ah yeah, I heard that the Philippine Arena is popular because of the size of the basketball court."
}
```

# Who are the annotators?

We would like to thank Imad Mousaoui, Ella Cho, Abdulmuizz Yusuf, and Parminder Singh Bharot, the professional annotators without whom this work would have not been possible.

## Licensing Information

MIT

## Citation

* CHARP:

```bibtex
@inproceedings{ghaddar-etal-2024-charp,
    title = "{CHARP}: Conversation History {A}wa{R}eness Probing for Knowledge-grounded Dialogue Systems",
    author = "Ghaddar, Abbas  and
      Alfonso-Hermelo, David  and
      Langlais, Philippe  and
      Rezagholizadeh, Mehdi  and
      Chen, Boxing  and
      Parthasarathi, Prasanna",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.90/",
    doi = "10.18653/v1/2024.findings-acl.90",
    pages = "1534--1551",
}
```

* CHARPEVAL:

```bibtex
@inproceedings{ghaddar-etal-2025-charpeval,
    title = "{CHARPEVAL}: Benchmarking Large Language Models' Contextual Reasoning in Knowledge-Grounded Dialogue",
    author = "Ghaddar, Abbas  and
      Alfonso-Hermelo, David  and
      Langlais, Philippe  and
      Chen, Boxing  and
      Parthasarathi, Prasanna",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.860/",
    pages = "16764--16775"
}

```