We lifted 2 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| iq-kaist/rm-lingual-transfer | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| is_openai_format | Ran | iq-kaist/rm-lingual-transfer/src/utils.py code served (permissive licence) · get_code("4253aac010c4504f") |
| map_chat_template_by_task | Not yet run | iq-kaist/rm-lingual-transfer/src/utils.py code served (permissive licence) · get_code("85071a4f3ab5213e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Reinforcement learning with human feedback (RLHF) is shown to largely benefit from precise reward models (RMs). However, recent studies in reward modeling schemes are skewed towards English, limiting the applicability of RLHF in multilingual alignments. In this work, we investigate the cross-lingual transfer of RMs trained in diverse languages, primarily from English. Our experimental results demonstrate the strong cross-lingual transfer of English RMs, exceeding target language RMs by 3~4% average increase in Multilingual RewardBench. Furthermore, we analyze the cross-lingual transfer of RMs through the representation shifts. Finally, we perform multilingual alignment to exemplify how cross-lingual transfer in RM propagates to enhanced multilingual instruction-following capability, along with extensive analyses on off-the-shelf RMs. We release the code, model, and data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.18027")
get_code_for_paper("2410.18027")
have("2410.18027")
Connect an agent — have() is free.