| license: mit | |
| # BioHopR | |
| [**Paper**]() | | |
| ## Description | |
| We introduce BioHopR, a novel benchmark designed to evaluate multi-hop, multi-answer reasoning in structured biomedical knowledge graphs. | |
| Built from the comprehensive PrimeKG, BioHopR includes 1-hop and 2-hop reasoning tasks that reflect real-world biomedical complexities. | |
| ## Prompt | |
| We used the below to get the response of the open source LLMs. | |
| ``` | |
| def generate_single(model, tokenizer, question): | |
| q="You are an expert biomedical researcher.\n"+question+"\nJust give me the answer without any explanations.\nAnswer:\n" | |
| inputs = tokenizer(q, return_tensors="pt", return_attention_mask=False).to(DEVICE) | |
| response = model.generate(**inputs, | |
| do_sample=False, | |
| temperature=0.0, | |
| top_p=None, | |
| num_beams=1, | |
| no_repeat_ngram_size=3, | |
| eos_token_id=tokenizer.eos_token_id, # End of sequence token | |
| pad_token_id=tokenizer.eos_token_id, # Pad token | |
| max_new_tokens=32, | |
| ) | |
| output = tokenizer.decode(response.squeeze()[len(inputs['input_ids'][0]):], skip_special_tokens=True) | |
| return output | |
| def generate_multi(model, tokenizer, question): | |
| q="You are an expert biomedical researcher.\n"+question+"\nJust give me the answers without any explanations in a bullet-pointed list.\nAnswer:\n" | |
| inputs = tokenizer(q, return_tensors="pt", return_attention_mask=False).to(DEVICE) | |
| response = model.generate(**inputs, | |
| do_sample=False, | |
| temperature=0.0, | |
| top_p=None, | |
| num_beams=1, | |
| no_repeat_ngram_size=3, | |
| eos_token_id=tokenizer.eos_token_id, # End of sequence token | |
| pad_token_id=tokenizer.eos_token_id, # Pad token | |
| max_new_tokens=256, | |
| ) | |
| output = tokenizer.decode(response.squeeze()[len(inputs['input_ids'][0]):], skip_special_tokens=True) | |
| return output | |
| ``` |