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GPT-4 Level Conversational QA Trained In a Few Hours

DocChat 최신버젼 다운로드

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# Cerebras DocChat: GPT-4 Level Conversational QA Trained In a Few Hours

This repository contains dataset preparation, training, and evaluation code for DocChat. The corresponding code for the LLM can be found in the `Cerebras-Llama3-DocChat` while code for the retriever is located at `Cerebras-Dragon-DocChat`.

The dataset preparation & training scripts are designed to be used with [Cerebras Model Zoo](https://github.com/Cerebras/modelzoo) Release 2.3. Note that although the training scripts _can_ be run on non-Cerebras hardware, they haven't been optimized for other hardware, and so your mileage may vary. The evaluation code does not depend on Model Zoo and is hardware agnostic (supports CPU & GPU) in order to ensure that everyone can evaluate our model (including those who don't have access to Cerebras machines).

Additional Links:
* [Blog post](https://www.cerebras.net/blog/train-a-gpt-4-level-conversational-qa-in-a-few-hours)
* [LLM model weights on HuggingFace](https://huggingface.co/cerebras/Llama3-DocChat-1.0-8B)
* Embedding model weights on HuggingFace: [Query Encoder](https://huggingface.co/cerebras/Dragon-DocChat-Query-Encoder), [Context Encoder](https://huggingface.co/cerebras/Dragon-DocChat-Context-Encoder)
* [Data preparation, training, and evaluation code](https://github.com/Cerebras/DocChat)

## About DocChat

We are excited to announce the release of Cerebras DocChat, our first iteration of models designed for document-based conversational question answering. This series includes two models: Cerebras Llama3-DocChat, a large language model (LLM), and Cerebras Dragon-DocChat, a multi-turn retriever model. These models were not only developed leveraging our deep ML expertise, but were also trained with remarkable speed. Using a single Cerebras System, Llama3-DocChat was trained in a few hours, while Cerebras Dragon-DocChat was fine-tuned in just *minutes* (yes you read that correctly).

**DocChat LLM**

Cerebras Llama3-DocChat was built on top of Llama 3 base using insights from the latest research on document-based Q&A, most notably Nvidia’s ChatQA model series. As part of this work, we leveraged our experience in LLM model training and dataset curation to overcome the gaps in ChatQA's released datasets and training recipes. Additionally, we employed synthetic data generation to address limitations that couldn't be fully resolved with the available real data.

**DocChat Retriever**

Similarly, Cerebras Dragon-DocChat was built on top of the Dragon+ model and trained on ChatQA’s conversational Q&A dataset. By finetuning using contrastive loss with hard negatives, we see absolute improvements in recall of 8.9% over Dragon+ and 3.5% over ChatQA Dragon-Multiturn respectively (top-1).

**Open Source Commitment**

In line with our commitment to open source, we are releasing not only the model weights but also the complete training recipes and associated datasets. This transparency allows the AI community to replicate, build upon, and innovate with our work. See below for links.

**Benchmarks**

The DocChat models have been evaluated across a variety of benchmarks, and achieve top of the line performance for their model sizes.

| **ChatRAG Benchmark** | **Llama3 Instruct 8B** | **Command-R-Plus** | **Nvidia Llama3-ChatQA 1.5 8B** | **GPT-4-Turbo-2024-04-09** | **Cerebras Llama3-DocChat 1.0 8B** |
| --- | --- | --- | --- | --- | --- |
| Doc2Dial | 31.33 | 33.51 | 39.33 | 35.35 | 39.19 |
| QuAC | 32.64 | 34.16 | 39.73 | 40.1 | 36 |
| QReCC | 43.4 | 49.77 | 49.03 | 51.46 | 50.27 |
| CoQA | 73.25 | 69.71 | 76.46 | 77.73 | 79.56 |
| DoQA | 30.34 | 40.67 | 49.6 | 41.6 | 48.77 |
| ConvFinQA | 53.15 | 71.21 | 78.46 | 84.16 | 80.13 |
| SQA | 36.6 | 74.07 | 73.28 | 79.98 | 74.19 |
| TopioCQA | 34.64 | 53.77 | 49.96 | 48.32 | 52.13 |
| HybriDial\* | 40.77 | 46.7 | 65.76 | 47.86 | 64 |
| INSCIT | 32.09 | 35.76 | 30.1 | 33.75 | 32.88 |
| Average (all) | 40.82 | 50.93 | 55.17 | 54.03 | 55.71 |
| Average (exclude HybriDial) | 40.83 | 51.4 | 53.99 | 54.72 | 54.79 |

| **Eleuther Eval Harness**  | **Llama3 Instruct 8B** | **Nvidia Llama3-ChatQA 1.5 8B** | **Cerebras Llama3-DocChat 1.0 8B** |
| --- | --- | --- | --- |
| hellaswag | 57.68 | 61.37 | 61.68 |
| winogrande | 71.98 | 73.95 | 74.11 |
| truthfulqa_mc1 | 36.23 | 28.52 | 29.25 |
| truthfulqa_mc2 | 51.65 | 43.56 | 45.14 |
| mmlu | 63.84 | 60.68 | 62.86 |
| gsm8k | 76.12 | 13.72 | 55.57 |
| arc_easy | 81.61 | 80.56 | 82.03 |
| arc_challenge | 52.99 | 51.02 | 53.92 |
| Average | 61.51 | 51.67 | 58.07 |

<table id="retriever-benchmarks">
   <tbody>
      <tr>
         <td rowspan="1" colspan="1">
            <div data-align="center">
               <strong data-renderer-mark="true">Benchmark</strong>
            </div>
         </td>
         <td rowspan="1" colspan="1">
            <strong data-renderer-mark="true">Metric</strong>
         </td>
         <td rowspan="1" colspan="1">
            <strong data-renderer-mark="true">Facebook Dragon+</strong>
         </td>
         <td rowspan="1" colspan="1">
            <strong data-renderer-mark="true">Nvidia Dragon-Multiturn</strong>
         </td>
         <td rowspan="1" colspan="1">
            <strong data-renderer-mark="true">Cerebras Dragon-DocChat</strong>
         </td>
      </tr>
      <tr>
         <td rowspan="3" colspan="1">
            Doc2Dial
         </td>
         <td rowspan="1" colspan="1">
            Recall@1
         </td>
         <td rowspan="1" colspan="1">
            43.95
         </td>
         <td rowspan="1" colspan="1">
            50.11
         </td>
         <td rowspan="1" colspan="1">
            51.54
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@5
         </td>
         <td rowspan="1" colspan="1">
            77.61
         </td>
         <td rowspan="1" colspan="1">
            83.85
         </td>
         <td rowspan="1" colspan="1">
            83.12
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@20
         </td>
         <td rowspan="1" colspan="1">
            92.05
         </td>
         <td rowspan="1" colspan="1">
            95.33
         </td>
         <td rowspan="1" colspan="1">
            95.25
         </td>
      </tr>
      <tr>
         <td rowspan="3" colspan="1">
            QuAC
         </td>
         <td rowspan="1" colspan="1">
            Recall@1
         </td>
         <td rowspan="1" colspan="1">
            62.09
         </td>
         <td rowspan="1" colspan="1">
            60.02
         </td>
         <td rowspan="1" colspan="1">
            61.30
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@5
         </td>
         <td rowspan="1" colspan="1">
            86.01
         </td>
         <td rowspan="1" colspan="1">
            86.51
         </td>
         <td rowspan="1" colspan="1">
            87.69
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@20
         </td>
         <td rowspan="1" colspan="1">
            96.48
         </td>
         <td rowspan="1" colspan="1">
            96.60
         </td>
         <td rowspan="1" colspan="1">
            97.25
         </td>
      </tr>
      <tr>
         <td rowspan="3" colspan="1">
            QReCC
         </td>
         <td rowspan="1" colspan="1">
            Recall@1
         </td>
         <td rowspan="1" colspan="1">
            49.00
         </td>
         <td rowspan="1" colspan="1">
            49.43
         </td>
         <td rowspan="1" colspan="1">
            55.41
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@5
         </td>
         <td rowspan="1" colspan="1">
            85.14
         </td>
         <td rowspan="1" colspan="1">
            86.6
         </td>
         <td rowspan="1" colspan="1">
            90.11
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@20
         </td>
         <td rowspan="1" colspan="1">
            97.21
         </td>
         <td rowspan="1" colspan="1">
            98.28
         </td>
         <td rowspan="1" colspan="1">
            98.39
         </td>
      </tr>
      <tr>
         <td rowspan="3" colspan="1">
            INSCIT*
         </td>
         <td rowspan="1" colspan="1">
            Recall@1
         </td>
         <td rowspan="1" colspan="1">
            11.13
         </td>
         <td rowspan="1" colspan="1">
            18.35
         </td>
         <td rowspan="1" colspan="1">
            21.65
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@5
         </td>
         <td rowspan="1" colspan="1">
            29.27
         </td>
         <td rowspan="1" colspan="1">
            48.45
         </td>
         <td rowspan="1" colspan="1">
            50.72
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@20
         </td>
         <td rowspan="1" colspan="1">
            49.07
         </td>
         <td rowspan="1" colspan="1">
            66.19
         </td>
         <td rowspan="1" colspan="1">
            72.78
         </td>
      </tr>
      <tr>
         <td rowspan="3" colspan="1">
            Topiocqa*
         </td>
         <td rowspan="1" colspan="1">
            Recall@1
         </td>
         <td rowspan="1" colspan="1">
            29.19
         </td>
         <td rowspan="1" colspan="1">
            31.34
         </td>
         <td rowspan="1" colspan="1">
            38.19
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@5
         </td>
         <td rowspan="1" colspan="1">
            62.52
         </td>
         <td rowspan="1" colspan="1">
            65.79
         </td>
         <td rowspan="1" colspan="1">
            72.47
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Recall@20
         </td>
         <td rowspan="1" colspan="1">
            83.69
         </td>
         <td rowspan="1" colspan="1">
            84.37
         </td>
         <td rowspan="1" colspan="1">
            87.23
         </td>
      </tr>
      <tr>
         <td rowspan="2" colspan="1">
            Average**
         </td>
         <td rowspan="1" colspan="1">
            Avg top 1
         </td>
         <td rowspan="1" colspan="1">
            49.36
         </td>
         <td rowspan="1" colspan="1">
            54.76
         </td>
         <td rowspan="1" colspan="1">
            58.29
         </td>
      </tr>
      <tr>
         <td rowspan="1" colspan="1">
            Avg top 5
         </td>
         <td rowspan="1" colspan="1">
            76.30
         </td>
         <td rowspan="1" colspan="1">
            81.50
         </td>
         <td rowspan="1" colspan="1">
            84.19
         </td>
      </tr>
   </tbody>
</table>

\*Evaluated on a subset of the wikipedia corpus that was available to us. All models use the same evaluation strategy to ensure apples-to-apples comparisons.

\*\* We follow the same convention as in ChatQA, where we compare top-5 and top-20 of TopiOCQA and INSCIT to top-1 and top-5, respectively, of the other datasets, in order match differences in average context length.

### The Recipe

While ChatQA provided a valuable foundation, we identified several gaps in their released datasets and training recipes. We crafted our final recipe by combining insights from analyzing their model as well as our own experience in LLM model training and dataset curation. Notably, we addressed the challenge of handling unanswerable questions, improving arithmetic performance and entity extraction:

* **Handling Unanswerable Questions**: In our initial attempts, the model struggled with unanswerable questions (i.e. responding “I can’t answer …”). The ChatQA paper notes that training on the synthetic conversational Q&A dataset yields worse performance on unanswerable benchmarks relative to when trained with their human dataset (which we don’t have access to); even so, the gap that we observed in initial experiments was substantial, requiring an alternative approach. Through experimentation, we found that upsampling the samples corresponding to unanswerable questions improves performance. Despite the improvement, the performance delta to SOTA on the QuAC and DoQA benchmarks (which test handling of unanswerable questions) show that there’s still room for further improvement.
* **Arithmetic Performance**: We observed that initial iterations of our model frequently made errors on arithmetic tasks (such as ConvFinQA) because it was trained to produce a final equation in a single shot. Inspired by Chain of Thought (CoT), we synthetically generated a CoT variant of the TAT-QA arithmetic dataset using Llama 3 70B Instruct which teaches the model to explicitly reason through it’s response before providing a final answer. We found that this addition led to a substantial boost in accuracy (+10 on ConvFinQA). We also mix in a small amount of the NuminaMath-CoT dataset, a collection of math problems with CoT solutions that range from high-school to olympiad difficulty levels, in order to improve our model’s GSM8k score.
* **Entity Extraction**: Our baseline performed poorly on entity extraction style tasks such as SQA, due to very few high quality samples that demonstrate the intended behavior. We solve this by mixing in a subset of SKGInstruct, an instruction tuning dataset constructed from a mix of structured knowledge grounding datasets.

### What’s Next?

Our model series demonstrates strong conversational Q&A and multi-turn retrieval capabilities, and we can’t wait to see what the community can achieve with it. We invite you to explore, experiment, and improve upon our work. There are many exciting directions we’re exploring that we plan to use this model series as the foundation for, such as long context support, improved math/reasoning, larger model sizes, etc. Stay tuned for more updates and improvements. 🚀

## Acknowledgements

DocChat was built on top of a large body of ML work, spanning training datasets, recipes, and evaluation. We want to thank each of these resources.

```
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}
@article{wu2023inscit,
  title={InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions},
  author={Wu, Zeqiu and Parish, Ryu and Cheng, Hao and Min, Sewon and Ammanabrolu, Prithviraj and Ostendorf, Mari and Hajishirzi, Hannaneh},
  journal={Transactions of the Association for Computational Linguistics},
  year={2023}
}
```

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