We lifted 8 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| likenneth/dialogue_action_token | canonical | 7 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| eval_actor | Ran | likenneth/dialogue_action_token/dat/pre_bc.py code served (permissive licence) · get_code("ca55c2bc3a455a58") |
| extract_leading_int | Ran | likenneth/dialogue_action_token/envs/redteam_env.py code served (permissive licence) · get_code("ff5a465969609b7b") |
| format_docstring | Ran | likenneth/dialogue_action_token/envs/sotopia_utils/utils.py code served (permissive licence) · get_code("f90433d593217a49") |
| get_bio | Ran | likenneth/dialogue_action_token/envs/sotopia_utils/utils.py code served (permissive licence) · get_code("aae3c57500fd1a36") |
| load_pickle | Ran | likenneth/dialogue_action_token/dat/td3.py code served (permissive licence) · get_code("38d71c4ed76282b3") |
| preprocess_function | Ran | likenneth/dialogue_action_token/dat/utils.py code served (permissive licence) · get_code("73e2c40bff6225ae") |
| preprocess_no_padding | Ran | likenneth/dialogue_action_token/dat/utils.py code served (permissive licence) · get_code("db147f8b7e6fcb02") |
| load_data | Not yet run | likenneth/dialogue_action_token/dat/bc.py code served (permissive licence) · get_code("3e6d732bbfaddc55") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present an approach called Dialogue Action Tokens (DAT) that adapts language model agents to plan goal-directed dialogues. The core idea is to treat each utterance as an action, thereby converting dialogues into games where existing approaches such as reinforcement learning can be applied. Specifically, we freeze a pretrained language model and train a small planner model that predicts a continuous action vector, used for controlled generation in each round. This design avoids the problem of language degradation under reward optimization. When evaluated on the Sotopia platform for social simulations, the DAT-steered LLaMA model surpasses GPT-4's performance. We also apply DAT to steer an attacker language model in a novel multi-turn red-teaming setting, revealing a potential new attack surface.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.11978")
get_code_for_paper("2406.11978")
have("2406.11978")
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