We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| wuyike2000/retrieve-rewrite-answer | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| adaptive_ntk_forward | Ran | wuyike2000/retrieve-rewrite-answer/KGQA-MetaQA/answer/attn_and_long_ctx_patches.py pointer only (licence: NONE) · get_code("2bb8091d29283e62") |
| apply_rotary_pos_emb_single | Ran | wuyike2000/retrieve-rewrite-answer/KGQA-MetaQA/answer/attn_and_long_ctx_patches.py pointer only (licence: NONE) · get_code("5067a5823022c273") |
| xformers_forward | Not yet run | wuyike2000/retrieve-rewrite-answer/KGQA-MetaQA/answer/attn_and_long_ctx_patches.py pointer only (licence: NONE) · get_code("6f776232403b9df9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite their competitive performance on knowledge-intensive tasks, large language models (LLMs) still have limitations in memorizing all world knowledge especially long tail knowledge. In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. However, their approaches lack a well-formed verbalization of KG knowledge, i.e., they ignore the gap between KG representations and textual representations. To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA. Based on this approach, we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. Experiments on several KGQA benchmarks show that the proposed KG-to-Text augmented LLMs approach outperforms previous KG-augmented LLMs approaches regarding answer accuracy and usefulness of knowledge statements.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.11206")
get_code_for_paper("2309.11206")
have("2309.11206")
Connect an agent — have() is free.