We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| yinzhangyue/selfaware | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| cut_sentences | Ran | yinzhangyue/selfaware/code/eval_model.py code served (permissive licence) · get_code("df081be962c9166d") |
| read_json | Ran | yinzhangyue/selfaware/code/run_model.py code served (permissive licence) · get_code("cd3c7b096ad71ffc") |
| remove_punctuation | Ran | yinzhangyue/selfaware/code/eval_model.py code served (permissive licence) · get_code("756ab533b15630b9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their performance within their existing knowledge. Despite their vast knowledge, LLMs are still limited by the amount of information they can accommodate and comprehend. Therefore, the ability to understand their own limitations on the unknows, referred to as self-knowledge, is of paramount importance. This study aims to evaluate LLMs' self-knowledge by assessing their ability to identify unanswerable or unknowable questions. We introduce an automated methodology to detect uncertainty in the responses of these models, providing a novel measure of their self-knowledge. We further introduce a unique dataset, SelfAware, consisting of unanswerable questions from five diverse categories and their answerable counterparts. Our extensive analysis, involving 20 LLMs including GPT-3, InstructGPT, and LLaMA, discovering an intrinsic capacity for self-knowledge within these models. Moreover, we demonstrate that in-context learning and instruction tuning can further enhance this self-knowledge. Despite this promising insight, our findings also highlight a considerable gap between the capabilities of these models and human proficiency in recognizing the limits of their knowledge.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.18153")
get_code_for_paper("2305.18153")
have("2305.18153")
Connect an agent — have() is free.