We lifted 8 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| hltchkust/univar | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| cohere_api | Ran | hltchkust/univar/value_eliciting_qa_generation/gen_answer_llm_api.py code served (permissive licence) · get_code("eb2ad3cbb871e2dc") |
| encode_qas | Ran | hltchkust/univar/univar_evaluation/utils.py code served (permissive licence) · get_code("f470fcd7a858340e") |
| encode_value_dataset | Ran | hltchkust/univar/univar_evaluation/utils.py code served (permissive licence) · get_code("0c8b356a9662e596") |
| openai_api | Ran | hltchkust/univar/value_eliciting_qa_generation/gen_answer_llm_api.py code served (permissive licence) · get_code("fe45f2f57cad641a") |
| seallm_chat_convo_format | Ran | hltchkust/univar/value_eliciting_qa_generation/gen_answer_llm.py code served (permissive licence) · get_code("c5ae1d59e9767e2a") |
| claude_api | Not yet run | hltchkust/univar/value_eliciting_qa_generation/gen_answer_llm_api.py code served (permissive licence) · get_code("590375b28ecf1908") |
| expand_sentence_transformer_encoder | Not yet run | hltchkust/univar/univar_evaluation/utils.py code served (permissive licence) · get_code("ee131ced5327bd24") |
| get_situation_qa | Not yet run | hltchkust/univar/value_eliciting_qa_generation/generate_prompt.py code served (permissive licence) · get_code("b332df303673d0d9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The widespread application of LLMs across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of human value alignment, there is an urgent need to understand the scope and nature of human values injected into these LLMs before their deployment and adoption. We propose UniVaR, a high-dimensional neural representation of symbolic human value distributions in LLMs, orthogonal to model architecture and training data. This is a continuous and scalable representation, self-supervised from the value-relevant output of 8 LLMs and evaluated on 15 open-source and commercial LLMs. Through UniVaR, we visualize and explore how LLMs prioritize different values in 25 languages and cultures, shedding light on complex interplay between human values and language modeling.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.07900")
get_code_for_paper("2404.07900")
have("2404.07900")
Connect an agent — have() is free.