File size: 6,284 Bytes
8817ead 6910c17 8817ead 7184df2 6910c17 7184df2 6910c17 7184df2 8817ead |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
---
base_model:
- Qwen/Qwen2.5-7B-Instruct
language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---
# Annotation-Efficient Universal Honesty Alignment
This is the official repository for the paper [Annotation-Efficient Universal Honesty Alignment](https://arxiv.org/abs/2510.17509).
Code: [https://github.com/Trustworthy-Information-Access/Annotation-Efficient-Universal-Honesty-Alignment](https://github.com/Trustworthy-Information-Access/Annotation-Efficient-Universal-Honesty-Alignment)
## Introduction
This repository provides modules that extend **Qwen2.5-7B-Instruct** with the ability to generate accurate confidence scores *before* response generation, indicating how likely the model is to answer a given question correctly across tasks. We offer two types of modules—**LoRA + Linear Head** and **Linear Head**—along with model parameters under three training settings:
1. **Elicitation (greedy):** Trained on all questions (over 560k) using self-consistency-based confidence annotations.
2. **Calibration-Only (right):** Trained on questions with explicit correctness annotations.
3. **EliCal (hybrid):** Initialized from the Elicitation model and further trained on correctness-labeled data.
For both **Calibration-Only** and **EliCal** settings, we provide models trained with different amounts of annotated data (1k, 2k, 3k, 5k, 8k, 10k, 20k, 30k, 50k, 80k, 200k, 560k+). Since **LoRA + Linear Head** is the main configuration used in our paper, the following description is based on this setup.
In our model, **LoRA is applied to all linear layers** with **r = 8** and **α = 16**. The **Linear Head** is added to the final layer of the model and takes as input the internal state of the **last token** from the final layer. It predicts a **confidence score between 0 and 1**, representing the model’s **estimated probability of answering the question correctly**.
# Model Architecture
```python
class LMWithVectorHead(nn.Module):
def __init__(self, model_name, lora_config, output_dim=1):
super().__init__()
backbone = AutoModel.from_pretrained(model_name, device_map='cpu')
# backbone.config.use_cache = False
self.peft_model = get_peft_model(backbone, lora_config)
self.config = backbone.config
hidden_size = backbone.config.hidden_size
self.vector_head = nn.Linear(hidden_size, output_dim) # 输出维度为 1
def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
"""启用梯度检查点，并处理可能的额外参数"""
self.peft_model.enable_input_require_grads()
if gradient_checkpointing_kwargs is not None:
self.peft_model.gradient_checkpointing_enable(**gradient_checkpointing_kwargs)
else:
self.peft_model.gradient_checkpointing_enable()
def forward(self, input_ids, attention_mask=None, labels=None):
# if hasattr(self.peft_model, "gradient_checkpointing"):
# print(f"✅ 梯度检查点已启用 - 当前模式: {self.peft_model.is_gradient_checkpointing}")
# else:
# print("❌ 梯度检查点未正确初始化")
outputs = self.peft_model(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True
)
# 获取最后一个 token 的隐藏状态
last_hidden = outputs.last_hidden_state # [B, T, H]
cls_hidden = last_hidden[:, -1, :] # [B, H]
logits = self.vector_head(cls_hidden) # [B, 1]
logits = torch.sigmoid(logits).squeeze(-1) # 添加 sigmoid 并压缩至 [B]
loss = None
if labels is not None:
loss_fct = nn.MSELoss() # 使用 MSE 损失
loss = loss_fct(logits, labels) # 计算 logits 和 labels 的 MSE
return CausalLMOutput(
loss=loss,
logits=logits
)
```
# Inference
This shows how to load the model. For more details, please refer to [Github Repo](https://github.com/Trustworthy-Information-Access/Annotation-Efficient-Universal-Honesty-Alignment/blob/master/honesty_alignment/eval_one_conf.py).
```python
base_model = AutoModel.from_pretrained(args.model_path)
# 2. 加载训练好的LoRA适配器到基础模型上
peft_model = PeftModel.from_pretrained(
base_model, # 使用基础模型，而不是model.peft_model
args.lora_path,
adapter_name="default"
)
# 3. 创建完整模型结构
lora_config = LoraConfig(
r=args.r,
lora_alpha=args.alpha,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj"],
lora_dropout=args.lora_dropout,
bias="none",
)
model = LMWithVectorHead(args.model_path, lora_config)
# 4. 替换为已加载LoRA的模型
model.peft_model = peft_model
# 5. 加载分类头权重
state_dict = torch.load(args.vector_head_path, map_location=device)
model.vector_head.load_state_dict(state_dict)
# 6. 激活适配器并移动到设备
model.peft_model.set_adapter("default")
model = model.to(device)
# 评估模式
model.eval()
```
# Files
```sh
/lora
├── greedy_answer_conf
│ └── long_qa
│ └── batchsize16_accumulation8_epochs10_weightdecay0.1_r8_alpha16_loradropout0.0 (training configuration)
│ ├── best_checkpoints
│ │ ├── lora_epoch_best/ # Path to LoRA module
│ │ └── vector_head_epoch_best.pt # Path to Linear Head weights
│ └── test_losses.json # Test loss for each epoch
│
├── hybrid_answer_conf
│ └── long_qa
│ ├── batchsize16_accumulation8_epochs10_weightdecay0.1_r8_alpha16_loradropout0.0 (560k samples)
│ ├── batchsize16_accumulation8_epochs50_weightdecay0.1_r8_alpha16_loradropout0.0_1k_training_samples (1k samples)
│ └── batchsize16_accumulation8_epochs50_weightdecay0.1_r8_alpha16_loradropout0.0_2k_training_samples (2k samples)
│
└── right_answer_conf
└── long_qa
└── ... # Same format as above
/mlp
...
``` |