README.md

---
license: apache-2.0
task_categories:
- text-retrieval
tags:
- long-context
- retrieval
- llm-evaluation
- benchmark
---

# Difficult Long-context Retrieval Tasks
* 📜 This is the Dataset used in the paper ["Hyper-multi-step: The Truth Behind Difficult Long-context Tasks"](https://arxiv.org/abs/2410.04422)
* 💻 [GitHub Repository](https://github.com/yuyijiong/hard_retrieval_for_llm)

This dataset is designed to evaluate the performance of Long-Context Language Models (LCLMs) on challenging retrieval tasks. While LCLMs are characterized by their extensive context windows, many long-context benchmarks present tasks that even the most advanced models struggle to complete. Our research indicates that the difficulty of these tasks primarily stems from two basic issues: "multi-matching retrieval," which requires the simultaneous retrieval of multiple items, and "logic-based retrieval," which necessitates logical judgment within retrieval criteria. These two problems, while seemingly straightforward, are proven to be hyper-multi-step in nature, explaining why LCLMs struggle with more advanced long-context tasks.

The tasks we provide are:

😄 Simple tasks which are easy for Long-Context LMs:
* ``simple_k2v``: Direct key-to-value retrieval. The key is given and the model needs to retrieve the corresponding value.
* ``simple_v2k``: Direct value-to-key retrieval. The value is given and the model needs to retrieve the corresponding key.
* ``multi_step(kv)``: multi-step (formal) KV retrieval. The model needs to retrieve multiple values with multiple queries. Then concatenate the values to form a new key, and finally retrieve the corresponding value.

😵 Difficult tasks which are nearly unsolvable for Long-Context LMs:
* ``logic(kv)``: logic-based KV retrieval. All the values are in range 0-9. We give the range of the value and the model needs to retrieve the corresponding key.
* ``logic(resume)``: logic-based student resume retrieval. We give the range of the GPA and the model needs to retrieve the corresponding student whose GPA is in the range.
* ``multi_match(kv)``: multi-match KV retrieval. The value is given and the model needs to retrieve multiple corresponding keys.
* ``multi_match(resume)``: multi-match student resume retrieval. A university name is given and the model needs to retrieve multiple corresponding students who are from this university.
* ``multi_match_last(kv)``: multi-match KV retrieval. The value is given and the model needs to retrieve multiple corresponding keys. The other gold keys are already given in the prompt, except the last one.


## The meaning of file names
For example:
* ``logic_kv_10`` means logic-based KV retrieval task with the context containing 10 KV items.
* ``3_match_resume_100`` means multi-match student resume retrieval task with the context containing 100 students and the model needs to retrieve 3 students.
* ``concat_3_kv_100_cot`` means multi-step KV retrieval task with the context containing 100 KV items and the model needs to concatenate 3 values retrieved with 3 queries. And the prompt style is Chain-of-Thought (CoT).

## Columns in the dataset
* ``prompt``: the full prompt of the task
* ``gold_keys``: the gold keys of the KV retrieval task. It's a string if there is only one gold key, otherwise it's a list of strings. In student resume retrieval, it's the student name (or a list of student names).
* ``gold_values``: the gold values of the KV retrieval task. It's a string if there is only one gold value, otherwise it's a list of strings. In student resume retrieval, it's the student's GPA or University (or a list of them).

Note that, in logic-based retrieval and multi-match retrieval tasks, ``gold_keys`` are actually the answer to the prompt.

## Sample Usage

You can use the `evaluate.py` script from the [GitHub repository](https://github.com/yuyijiong/hard_retrieval_for_llm) to test the performance of LLMs on these difficult retrieval tasks or other retrieval tasks. You should directly modify the code in `evaluate.py` to choose different tasks, models, and prompt types.

The prompt styles provided are:
*   `None`: default prompt, lets the model give the answer directly.
*   `"cot"`: adds a Chain-of-Thought (CoT) prompt, guiding the model to 'think step by step'.
*   `"one-by-one"`: adds a one-by-one prompt, guiding the model to 'examine every item one by one'.

For more detailed usage instructions, including hidden states linear probing and attention analysis, please refer to the [GitHub repository](https://github.com/yuyijiong/hard_retrieval_for_llm).